Difference between revisions of "Decentralized: Federated & Distributed"

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|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS  
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|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools  
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[http://www.youtube.com/results?search_query=Federated+Learning+deep+machine+learning+ML Youtube search...]
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[https://www.youtube.com/results?search_query=ai+Federated+Learning+deep+machine+learning+ML YouTube]
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[http://www.google.com/search?q=Federated+Learning+deep+machine+learning+ML ...Google search]
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[https://www.quora.com/search?q=ai%20Federated%20Learning%20deep%20machine%20learning%20ML ... Quora]
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[https://www.google.com/search?q=ai+Federated+Learning+deep+machine+learning+ML ...Google search]
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[https://news.google.com/search?q=ai+Federated+Learning+deep+machine+learning+ML ...Google News]
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[https://www.bing.com/news/search?q=ai+Federated+Learning+deep+machine+learning+ML&qft=interval%3d%228%22 ...Bing News]
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 +
* [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
 +
* [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
 +
* [[Blockchain]]
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* [[Memory]]
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* [[OpenMined]]
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* [[Sakana]] ... inspired by the way that fish and other animals work together in groups
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* [[Distributed Deep Reinforcement Learning (DDRL)]]
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* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 +
* [[Telecommunications]] ... [[Computer Networks]] ... [[Telecommunications#5G|5G]] ... [[Satellite#Satellite Communications|Satellite Communications]] ... [[Quantum Communications]] ... [[Agents#Communication | Agents]] ... [[AI Generated Broadcast Content|AI Broadcast; Radio, Stream, TV]]
  
−
* [[Distributed]] Learning -- high performance technique
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−
* [http://en.wikipedia.org/wiki/Federated_learning Federated Learning | Wikipedia]
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= Centralized vs. Decentralized vs. Distributed =
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* [http://medium.com/syncedreview/federated-learning-the-future-of-distributed-machine-learning-eec95242d897 Federated Learning: The Future of Distributed Machine Learning | Mi Zhang - Medium]
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[https://www.youtube.com/results?search_query=Decentralized+Federated+Distributed+Learning+deep+machine+learning+AI Youtube search...]
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* [http://arxiv.org/pdf/1902.04885.pdf Federated Machine Learning: Concept and Applications | Q. Yang, Y. Liu, T. Chen and Y. Tong]
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[https://www.google.com/search?q=Decentralized+Federated+Distributed+Learning+deep+machine+learning+AI ...Google search]
−
* [http://arxiv.org/abs/1905.06731 BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning | A. G. Roy, S. Siddiqui, S. Pölsterl, N. Navab, and C. Wachinger]
+
 
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* [http://www.researchgate.net/publication/329758083_Chained_Anomaly_Detection_Models_for_Federated_Learning_An_Intrusion_Detection_Case_Study Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study | D. Preuveneers, V. Rimmer, I. Tsingenopoulos, J. Spooren, W. Joosen and E. Ilie-Zudor]
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* [https://www.fliphodl.com/social-media-alternatives-series-ep-1-what-you-need-to-know/ Social Media Alternatives Series, EP. 1: What You NEED to Know | Fliphodl]
−
* [http://hackernoon.com/a-beginners-guide-to-federated-learning-b29e29ba65cf A Beginners Guide to Federated Learning | Santanu Bhattacharya]
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* [https://www.fliphodl.com/exclusive-we-tried-ultra-and-it-will-kill-steam/ Exclusive: We tried Ultra and it will kill Steam | Fliphodl]
 +
 
 +
https://upload.wikimedia.org/wikipedia/commons/b/ba/Centralised-decentralised-distributed.png
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== Federated ==
 +
[https://www.youtube.com/results?search_query=Federated+Learning+deep+machine+learning+ML Youtube search...]
 +
[https://www.google.com/search?q=Federated+Learning+deep+machine+learning+ML ...Google search]
 +
 
 +
* [https://en.wikipedia.org/wiki/Federated_learning Federated Learning | Wikipedia]
 +
* [https://arxiv.org/pdf/1902.04885.pdf Federated Machine Learning: Concept and Applications | Q. Yang, Y. Liu, T. Chen and Y. Tong]
 +
* [https://medium.com/syncedreview/federated-learning-the-future-of-distributed-machine-learning-eec95242d897 Federated Learning: The Future of Distributed Machine Learning | Mi Zhang - Medium]
 +
* [https://www.researchgate.net/publication/329758083_Chained_Anomaly_Detection_Models_for_Federated_Learning_An_Intrusion_Detection_Case_Study Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study | D. Preuveneers, V. Rimmer, I. Tsingenopoulos, J. Spooren, W. Joosen and E. Ilie-Zudor]
 +
* [https://hackernoon.com/a-beginners-guide-to-federated-learning-b29e29ba65cf A Beginners Guide to Federated Learning | Santanu Bhattacharya]
 
* [[Watch me Build a Cybersecurity Startup]]
 
* [[Watch me Build a Cybersecurity Startup]]
 
* Federated Learning Frameworks:
 
* Federated Learning Frameworks:
−
** [[OpenMined]] Pysyft  ...[http://github.com/OpenMined/PySyft/tree/master/examples/tutorials GitHub]
+
** [[OpenMined]] Pysyft  ...[https://github.com/OpenMined/PySyft/tree/master/examples/tutorials GitHub]
−
** [http://www.tensorflow.org/federated TensorFlow Federated Learning]  ...* [http://venturebeat.com/2019/03/06/tensorflow-federated-allows-machine-learning-models-to-train-on-data-from-different-locations/ TensorFlow Federated] helps train AI models on data from different locations | Kyle Wiggers - VentureBeat     
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** [https://www.tensorflow.org/federated TensorFlow Federated Learning]  ...* [https://venturebeat.com/2019/03/06/tensorflow-federated-allows-machine-learning-models-to-train-on-data-from-different-locations/ TensorFlow Federated] helps train AI models on data from different locations | Kyle Wiggers - VentureBeat     
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** [http://www.fedai.org/ Federated AI Ecosystem (FATE) | FEDAI.org]
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** [https://www.fedai.org/ Federated AI Ecosystem (FATE) | FEDAI.org]
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** [http://devblogs.nvidia.com/federated-learning-clara/ Clara Federated Learning | NVIDIA]
+
** [https://devblogs.nvidia.com/federated-learning-clara/ Clara Federated Learning | NVIDIA]
 +
 
  
 +
<img src="https://questforai.files.wordpress.com/2019/03/federated_learning_animated_labeled.gif" width="400">
 +
<img src="https://www.searchtechnologies.com/images/federated-search1.png" width="300">
  
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<img src="http://questforai.files.wordpress.com/2019/03/federated_learning_animated_labeled.gif" width="500" height="500">
 
  
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<img src="http://cdn-images-1.medium.com/max/2304/1*P2qr6R_VcRE22tnKZpLj_A.png" width="900" height="500">
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<img src="https://cdn-images-1.medium.com/max/2304/1*P2qr6R_VcRE22tnKZpLj_A.png" width="900">
  
 
<youtube>89BGjQYA0uE</youtube>
 
<youtube>89BGjQYA0uE</youtube>
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* [http://venturebeat.com/2019/10/13/nvidia-uses-federated-learning-to-create-medical-imaging-ai/ NVIDIA (King’s College London) uses federated learning to create medical imaging AI | Khari Johnson - VentureBeat]
+
* [https://venturebeat.com/2019/10/13/nvidia-uses-federated-learning-to-create-medical-imaging-ai/ NVIDIA (King’s College London) uses federated learning to create medical imaging AI | Khari Johnson - VentureBeat]
 
<youtube>Jy7ozgwovgg</youtube>
 
<youtube>Jy7ozgwovgg</youtube>
  
 +
== <span id="Distributed"></span>Distributed ==
 +
[https://www.youtube.com/results?search_query=Distributed+Learning+deep+machine+learning+ML Youtube search...]
 +
[https://www.google.com/search?q=Distributed+Learning+deep+machine+learning+ML ...Google search]
  
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= Privacy Preserving Machine Learning (PPML) Techniques =
+
* [[Distributed Deep Reinforcement Learning (DDRL)]]
−
[http://www.youtube.com/results?search_query=Secure+Multiparty+Computation+Privacy+Preserving+Machine+Learning+PPML+Techniques Youtube search...]
+
* [https://learn.microsoft.com/en-us/azure/architecture/example-scenario/ai/training-python-models Distributed hyperparameter tuning for machine learning models | Microsoft]
−
[http://www.google.com/search?q=Secure+Multiparty+Computation+Privacy+Preserving+Machine+Learning+PPML+Techniques ...Google search]
 
−
 
 
−
Many privacy-enhancing techniques concentrated on allowing multiple input parties to collaboratively train ML models without releasing their private data in its original form. This was mainly performed by utilizing cryptographic approaches, or differentially-private data release (perturbation techniques). Differential privacy is especially effective in preventing membership inference attacks. [http://arxiv.org/ftp/arxiv/papers/1804/1804.11238.pdf Privacy Preserving Machine Learning: Threats and Solutions | Mohammad Al-Rubaie - Iowa State University]
 
−
 
 
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Multiparty Computation (MPC) enables computation on data from different providers/parties, such that the other participating parties gain no additional information about each others’ inputs, except what can be learned from the public output of the algorithm. In other words, when we have the parties Alice, Bob and Casper, all three have access to the output. However, it is not possible for, e.g., Alice to know the plain data Bob and Casper provided.  [http://medium.com/@apfelbeck.florian/secure-multiparty-computation-enabling-privacy-preserving-machine-learning-ffef396b8ca2 Secure Multiparty Computation — Enabling Privacy-Preserving Machine Learning | Florian Apfelbeck - Medium]
 
−
 
 
−
==== DeepSecure ====
 
−
a framework that enables scalable execution of the state-of-the-art Deep Learning (DL) models in a privacy-preserving setting. DeepSecure targets scenarios in which neither of the involved parties including the cloud servers that hold the DL model parameters or the delegating clients who own the data is willing to reveal their information. Our framework is the first to empower accurate and scalable DL analysis of data generated by distributed clients without sacrificing the security to maintain efficiency. The secure DL computation in DeepSecure is performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized realization of various components used in DL. Our optimized implementation achieves more than 58-fold higher throughput per sample compared with the best-known prior solution. In addition to our optimized GC realization, we introduce a set of novel low-overhead pre-processing techniques which further reduce the GC overall runtime in the context of deep learning. Extensive evaluations of various DL applications demonstrate up to two orders-of-magnitude additional runtime improvement achieved as a result of our pre-processing methodology. This paper also provides mechanisms to securely delegate GC computations to a third party in constrained embedded settings. [http://arxiv.org/abs/1705.08963 DeepSecure: Scalable Provably-Secure Deep Learning | B. Rouhani, M. S. Riazi, and F. Koushanfar]
 
  
−
==== SecureML ====
+
Distributed machine learning refers to multi-node machine learning algorithms and systems that are designed to improve performance, increase accuracy, and scale to larger input data sizes. Increasing the input data size for many algorithms can significantly reduce the learning error and can often be more effective than using more complex methods [8]. Distributed machine learning allows companies, researchers, and individuals to make informed decisions and draw meaningful conclusions from large amounts of data. Many systems exist for performing machine learning tasks in a distributed environment. These systems fall into three primary categories: database, general, and purpose-built systems. Each type of system has distinct advantages and disadvantages, but all are used in practice depending upon individual use cases, performance requirements, input data sizes, and the amount of implementation effort. | [https://link.springer.com/referenceworkentry/10.1007%2F978-1-4614-8265-9_80647 SpringerLink]
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[http://eprint.iacr.org/2017/396.pdf SecureML: A System for Scalable Privacy-Preserving Machine Learning | Payman Mohassel & Yupeng Zhang]
 
  
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==== MiniONN ====
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{|<!-- T -->
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[http://eprint.iacr.org/2017/452.pdf Oblivious Neural Network Predictions via MiniONN transformations | J. Liu, M. Juuti, Y. Lu and N. Asokan]
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{| class="wikitable" style="width: 550px;"
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<youtube>jkkmBpJ-Eeo</youtube>
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<b>Distinguished Lecturer : Eric Xing - Strategies & Principles for Distributed Machine Learning
 +
</b><br>The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters that promise adequate capacity to digest massive datasets and offer powerful [[Predictive Analytics]] (such as high-dimensional [[latent]] features, intermediate representations, and decision functions) thereupon. In order to run ML algorithms at such scales, on a distributed cluster with 10s to 1000s of machines, it is often the case that significant engineering efforts are required --- and one might fairly ask if such engineering truly falls within the domain of ML research or not. Taking the view that Big ML systems can indeed benefit greatly from ML-rooted statistical and algorithmic insights --- and that ML researchers should therefore not shy away from such systems design --- we discuss a series of principles and strategies distilled from our resent effort on industrial-scale ML solutions that involve a continuum from application, to engineering, and to theoretical research and [[development]] of Big ML system and architecture, on how to make them efficient, general, and with convergence and scaling guarantees. These principles concern four key questions which traditionally receive little attention in ML research: How to distribute an ML program over a cluster? How to bridge ML computation with inter-machine [[Agents#Communication | communication]]? How to perform such [[Agents#Communication | communication]]? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typical in traditional computer programs, and by dissecting successful cases of how we harness these principles to design both high-performance distributed ML software and general-purpose ML framework, we present opportunities for ML researchers and practitioners to further shape and grow the area that lies between ML and systems. This is joint work with the CMU Petuum Team.
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<youtube>bRMGoPqsn20</youtube>
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<b>Distributed [[TensorFlow]] training (Google I/O '18)
 +
</b><br>To efficiently train machine learning models, you will often need to scale your training to multiple GPUs, or even multiple machines. [[TensorFlow]] now offers rich functionality to achieve this with just a few lines of code. Join this session to learn how to set this up.  
  
−
==== ABY3 ====
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Distribution Strategy API:
 +
https://goo.gl/F9vXqQ
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https://goo.gl/Zq2xvJ
  
 +
ResNet50 Model Garden example with MirroredStrategy API:
 +
https://goo.gl/3UWhj8
  
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Performance Guides:
 +
https://goo.gl/doqGE7
 +
https://goo.gl/NCnrCn
  
 +
Commands to set up a GCE instance and run distributed training:
 +
https://goo.gl/xzwN4C
  
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== Cryptographic Approaches ==
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Multi-machine distributed training with train_and_evaluate:
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[http://www.youtube.com/results?search_query=Cryptographic+Approaches+machine+learning+ML Youtube search...]
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https://goo.gl/kyikAC
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[http://www.google.com/search?q=Cryptographic+Approaches+machine+learning+ML ...Google search]
 
  
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When a certain ML application requires data from multiple input parties, cryptographic protocols could be utilized to perform ML training/testing on encrypted data. In many of these techniques, achieving better efficiency involved having data owners contribute their encrypted data to the computation servers, which would reduce the problem to a secure two/three party computation setting. In addition to increased efficiency, such approaches have the benefit of not requiring the input parties to remain online. [http://arxiv.org/ftp/arxiv/papers/1804/1804.11238.pdf Privacy Preserving Machine Learning: Threats and Solutions | Mohammad Al-Rubaie - Iowa State University]
+
Watch more [[TensorFlow]] sessions from I/O '18 here → https://goo.gl/GaAnBR
 +
See all the sessions from Google I/O '18 here → https://goo.gl/q1Tr8x
  
−
=== Homomorphic Encryption ===
+
Subscribe to the [[TensorFlow]] channel → https://goo.gl/ht3WGe
−
[http://www.youtube.com/results?search_query=Homomorphic+Encryption+machine+learning+ML Youtube search...]
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|}
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[http://www.google.com/search?q=Homomorphic+Encryption+machine+learning+ML ...Google search]
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<youtube>xtxxLWZznBI</youtube>
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<b>Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
 +
</b><br>In this video from 2018 Swiss HPC Conference, Torsten Hoefler from (ETH) Zürich presents: Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis. "[[Neural Network#Deep Neural Network (DNN)|Deep Neural Networks (DNN)]] are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this talk, we describe the problem from a theoretical [[perspective]], followed by approaches for its parallelization. Specifically, we present trends in DNN architectures and the resulting implications on parallelization strategies. We discuss the different types of concurrency in DNNs; synchronous and asynchronous stochastic gradient descent; distributed system architectures; [[Agents#Communication | communication]] schemes; and performance modeling. Based on these approaches, we extrapolate potential directions for parallelism in deep learning." Learn more: https://hpcadvisorycouncil.com  Sign up for our insideHPC Newsletter: https://insidehpc.com/newsletter
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<youtube>-nI87_cDGcM</youtube>
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<b>Machine Learning Systems for Highly Distributed and Rapidly Growing Data
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</b><br>[[Microsoft Research]] The usability and practicality of machine learning are largely influenced by two critical factors: low latency and low cost. However, achieving low latency and low cost is very challenging when machine learning depends on real-world data that are rapidly growing and highly distributed (e.g., training a face recognition model using pictures stored across many data centers globally). In this talk, I will present my work on building low-latency and low-cost machine learning systems that enable efficient processing of real-world, large-scale data. I will describe a system-level approach that is inspired by the general characteristics of machine learning algorithms, machine learning model structures, and machine learning training/serving data. In line with this approach, I will first present a system that provides both low-latency and low-cost machine learning serving (inferencing) over large-scale continuously-growing datasets (e.g. videos). Shifting the focus to model training, I will then present a system that makes machine learning training over geo-distributed datasets as fast as training within a single data center. Finally, I will discuss our ongoing efforts to tackle a fundamental and largely overlooked problem: machine learning training over skewed data partitions (e.g., facial images collected by cameras in different countries).
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Fully homomorphic encryption enables the computation on encrypted data, with operations such as addition and multiplication that can be used as basis for more complex arbitrary functions. Due to the high cost associated with frequently bootstrapping the cipher text (refreshing the cipher text because of the accumulated noise), additive homomorphic encryption schemes were mostly used in PPML approaches. Such schemes only enable addition operations on encrypted data, and multiplication by a plain text. [http://arxiv.org/ftp/arxiv/papers/1804/1804.11238.pdf Privacy Preserving Machine Learning: Threats and Solutions | Mohammad Al-Rubaie - Iowa State University]  
+
=== <span id="Peer-to-Peer"></span>Peer-to-Peer ===
 +
[https://www.youtube.com/results?search_query=Peer-to-Peer+deep+machine+learning+ML Youtube search...]
 +
[https://www.google.com/search?q=Peer-to-Peer+deep+machine+learning+ML ...Google search]
  
−
=== Garbled Circuits ===
+
* [[Hopfield Network (HN)]]
−
[http://www.youtube.com/results?search_query=Garbled+Circuits+machine+learning+ML Youtube search...]
+
* [[Loop#Feedback Loop - Peer Learning|Feedback Loop - Peer Learning]]
−
[http://www.google.com/search?q=Garbled+Circuits+machine+learning+ML ...Google search]
+
* [https://link.springer.com/chapter/10.1007/978-3-642-44958-1_35 Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory | A. Amir, A. Amin, and A. Khan - SpingerLink]  ...Distributed Associative Memory Tree (DASMET), to deal with multi-feature recognition in a peer-to-peer (P2P)-based system.
 +
* [https://arxiv.org/abs/1905.06731 BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning | A. G. Roy, S. Siddiqui, S. Pölsterl, N. Navab, and C. Wachinger]
 +
* [https://blog.imarticus.org/role-of-peer-to-peer-networks-in-creating-transparency-and-increased-usage-of-ai/ Role of Peer to Peer Networks In Creating Transparency And Increased Usage of AI | Imarticus]
 +
* [https://mas.cs.umass.edu/Documents/HZHANG_AAMAS07.pdf A Reinforcement Learning based Distributed Search Algorithm For Hierarchical Peer-to-Peer Information Retrieval Systems | Haizheng Zhang and Victor Lesser]  
  
−
Assuming a two-party setup with Alice and Bob wanting to obtain the result of a function computed on their private inputs, Alice can convert the function into a garbled circuit, and send this circuit along with her garbled input. Bob obtains the garbled version of his input from Alice without her learning anything about Bob’s private input (e.g., using oblivious transfer). Bob can now use his garbled input with the garbled circuit to obtain the result of the required function (and can optionally share it with Alice). Some PPML approaches combined additive homomorphic encryption with Garbled circuits. [http://arxiv.org/ftp/arxiv/papers/1804/1804.11238.pdf Privacy Preserving Machine Learning: Threats and Solutions | Mohammad Al-Rubaie - Iowa State University]
 
  
−
=== Secret Sharing ===
+
<img src="https://www.henry.pupil.me.uk/Images/Peer%20to%20peer.png" width="300">
−
[http://www.youtube.com/results?search_query=Secret+Sharing+machine+learning+ML Youtube search...]
 
−
[http://www.google.com/search?q=Secret+Sharing+machine+learning+ML ...Google search]
 
  
−
A method for distributing a secret among multiple parties, with each one holding a “share” of the secret. Individual shares are of no use on their own; however, when the shares are combined, the secret can be reconstructed. With threshold secret sharing, not all the “shares” are required to reconstruct the secret; but only “t” of them (“t” refers to threshold).
 
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In one setting, multiple input parties can generate “shares” of their private data, and send these shares to a set of non-colluding computation servers. Each server could compute a “partial result” from the “shares” it received. Finally, a results’ party (or a proxy) can receive these partial results, and combine them to find the final result. [http://arxiv.org/ftp/arxiv/papers/1804/1804.11238.pdf Privacy Preserving Machine Learning: Threats and Solutions | Mohammad Al-Rubaie - Iowa State University]
 
  
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=== Secure Processors ===
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[http://www.youtube.com/results?search_query=Secure+Processors+machine+learning+ML Youtube search...]
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[http://www.google.com/search?q=Secure+Processors+machine+learning+ML ...Google search]
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<youtube>8sN3cT5T-5g</youtube>
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<b>Introduction to Decentralized P2P Apps
 +
</b><br>Most people think peer-to-peer (P2P) networks are just for file sharing, but it turns out you can also build other types of applications on P2P networks with advantages like enhanced [[privacy]] and security. We’ll walk through the process of building an increasingly complex P2P cloud storage system (think Dropbox), and touch on the challenges you’d run into and some of their possible solutions. Topics include efficiently locating data within a large network and building a system where we can trust random people on the internet with our personal files. EVENT: SFNode Meetup July 2018 SPEAKER: Dylan Barnard  PERMISSIONS: SFNode Meetup Organizer provided Coding Tech with the permission to republish this video.
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<youtube>ie-qRQIQT4I</youtube>
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<b>What is a Peer to Peer Network? Blockchain P2P Networks Explained
 +
</b><br>A peer to peer network, often referred to as p2p network, is one of the key aspects of blockchain technology. In this video, we break down the complexity of peer to peer networks by first defining what a network is and how p2p networks differ from traditional networks. [https://lisk.io/what-is-blockchain Learn more about P2P Networks]  
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While initially introduced to ensure the confidentiality and integrity of sensitive code from unauthorized access by rogue software at higher privilege levels, Intel SGXprocessor are being utilized in privacy-preserving computation. Ohrimenko et al.14 developed a data oblivious ML algorithms for neural networks, SVM, k-means clustering, decision trees and matrix factorization that are based on SGX-processors. The main idea involves having multiple data owners collaborate to perform one of the above mentioned ML tasks with the computation party running the ML task on an SGX-enabled data center. An adversary can control all the hardware and software in the data center except for the SGX-processors used for computation. In this system, each data owner independently establishes a secure channel with the enclave (containing the code and data), authenticates themselves, verifies the integrity of the ML code in the cloud, and securely uploads its private data to the enclave. After all the data is uploaded, the ML task is run by the secure processor, and the output is sent to the results’ parties over secure authenticated channels. [http://arxiv.org/ftp/arxiv/papers/1804/1804.11238.pdf Privacy Preserving Machine Learning: Threats and Solutions | Mohammad Al-Rubaie - Iowa State University]  
+
=== <span id="Proxy"></span>Proxy ===
 +
[https://www.youtube.com/results?search_query=Proxy+deep+machine+learning+ML+AI Youtube search...]
 +
[https://www.google.com/search?q=Proxy+deep+machine+learning+ML+AI ...Google search]
  
−
== Perturbation Approaches ==
+
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[http://www.youtube.com/results?search_query=Perturbation+Approaches+machine+learning+ML Youtube search...]
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[http://www.google.com/search?q=Perturbation+Approaches+machine+learning+ML ...Google search]
+
{| class="wikitable" style="width: 550px;"
 +
||
 +
<youtube>ozhe__GdWC8</youtube>
 +
<b>Proxy vs. Reverse Proxy (Explained by Example)
 +
</b><br>Hussein Nasser In this episode we explain the difference between a Proxy (Forward proxy) and Reverse Proxy by example, and list all the benefits of each server.  Online diagram tool used in this video: Http://www.gliffy.com  
 +
|}
 +
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<youtube>SyLE5IF75lQ</youtube>
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<b>Proxy vs. Peer-to-Peer (P2P) Connections | remote.it webinar
 +
</b><br>In this webinar, we will explain how each connection type works, and in what applications you may prefer to use one or the other. You will learn how to use remote.it on Windows or macOS and on mobile (iOS/Android) apps to make P2P connections while we present the advantages of P2P versus traditional proxy connections from the remote.it web portal. All while making port forwardless connections.
 +
|}
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Differential privacy (DP) techniques resist membership inference attacks by adding random noise to the input data, to iterations in a certain algorithm, or to the algorithm output. While most DP approaches assume a trusted aggregator of the data, local differential privacy allows each input party to add the noise locally; thus, requiring no trusted server. Finally, dimensionally reduction perturbs the data by projecting it to a lower dimensional hyperplane to prevent reconstructing the original data, and/or to restrict inference of sensitive information. [http://arxiv.org/ftp/arxiv/papers/1804/1804.11238.pdf Privacy Preserving Machine Learning: Threats and Solutions | Mohammad Al-Rubaie - Iowa State University]  
+
=== <span id="Submarine Scenario"></span>Submarine Scenario ===
 +
[https://www.youtube.com/results?search_query=D-DIL+denied+disconnected+intermittent+cloud+machine+learning+ML+AI Youtube search...]
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[https://www.google.com/search?q=D-DIL+denied+disconnected+intermittent+cloud+machine+learning+ML+AI ...Google search]
  
−
=== Differential Privacy (DP) ===
+
* [https://azure.microsoft.com/en-us/products/azure-stack Azure Stack] 
−
[http://www.youtube.com/results?search_query=Differential+Privacy+machine+learning+ML Youtube search...]
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** [https://learn.microsoft.com/en-us/azure/architecture/guide/technology-choices/hybrid-considerations Azure hybrid options | ][[Microsoft]]
−
[http://www.google.com/search?q=Differential+Privacy+machine+learning+ML ...Google search]
+
** [https://azure.microsoft.com/en-us/products/azure-stack/hub/ Azure Stack Hub]
 +
*** [https://learn.microsoft.com/en-us/azure-stack/operator/azure-stack-datacenter-integration?source=recommendations&view=azs-2206 Datacenter integration planning considerations for Azure Stack Hub integrated systems | ][[Microsoft]]
 +
**** [https://aka.ms/azstackcapacityplanner Azure Stack Hub capacity planner spreadsheet | ][[Microsoft]]
 +
**** [https://azure.microsoft.com/mediahandler/files/resourcefiles/azure-stack-hub-licensing-packaging-pricing-guide/Azure%20Stack%20Hub%20Licensing%20Packaging%20and%20Pricing%20Guide.pdf Azure Stack Hub Licensing, Packaging & Pricing Guide | ][[Microsoft]]
 +
*** [https://learn.microsoft.com/en-us/azure-stack/operator/azure-stack-disconnected-deployment?view=azs-2206 Azure disconnected deployment planning decisions for Azure Stack Hub integrated systems ][[Microsoft]]  ... Features that are impaired or unavailable in disconnected deployments
 +
*** [https://learn.microsoft.com/en-us/azure/architecture/solution-ideas/articles/hybrid-relay-connection Hybrid relay connection in Azure and Azure Stack Hub | ][[Microsoft]]
 +
*** [https://learn.microsoft.com/en-us/azure/architecture/solution-ideas/articles/ai-at-the-edge AI at the edge with Azure Stack Hub | ][[Microsoft]]
 +
**** [https://learn.microsoft.com/en-us/azure/architecture/guide/iot/machine-learning-inference-iot-edge Enable machine learning inference on an Azure IoT Edge device | ][[Microsoft]]
 +
**** [https://learn.microsoft.com/en-us/azure/architecture/solution-ideas/articles/ai-at-the-edge-disconnected Disconnected AI at the edge with Azure Stack Hub | ][[Microsoft]]
 +
**** [https://learn.microsoft.com/en-us/azure/architecture/hybrid/deploy-ai-ml-azure-stack-edge Deploy AI and machine learning computing on-premises and to the edge | ][[Microsoft]]
 +
**** [https://learn.microsoft.com/en-us/azure/machine-learning/reference-machine-learning-cloud-parity Azure Machine Learning feature availability across clouds regions | ][[Microsoft]]
 +
** [https://learn.microsoft.com/en-us/azure-stack/hci/concepts/stretched-clusters Stretched clusters overview | ][[Microsoft]]
 +
*** [https://learn.microsoft.com/en-us/azure/architecture/hybrid/azure-stack-hci-dr Use Azure Stack HCI stretched clusters for disaster recovery | ][[Microsoft]]
 +
** [https://learn.microsoft.com/en-us/azure/architecture/hybrid/hybrid-perf-monitoring Hybrid availability and performance monitoring | ][[Microsoft]]
 +
** [https://learn.microsoft.com/en-us/azure/architecture/hybrid/azure-automation-hybrid Azure Automation in a hybrid environment | ][[Microsoft]]
 +
** [https://learn.microsoft.com/en-us/azure/architecture/hybrid/azure-file-share Use Azure file shares in a hybrid environment | ][[Microsoft]]
 +
* [https://dodcio.defense.gov/Portals/0/Documents/DoD-C3-Strategy.pdf Command, Control, and Communications (C3) Modernization Strategy | Department of] [[Defense]]
  
−
Differential privacy is a powerful tool for quantifying and solving practical problems related to privacy. Its flexible definition gives it the potential to be applied in a wide range of applications, including Machine Learning applications. [http://towardsdatascience.com/understanding-differential-privacy-85ce191e198a Understanding Differential Privacy - From Intuitions behind a Theory to a Private AI Application | An Nguyen - Towards Data Science]
+
[[Microsoft]]’s SharePoint, Exchange, and Office 365 products run on Azure and Azure Stack, as do [[Microsoft]]’s database, e-commerce, and software [[development]] products. Extend Azure services and capabilities to your environment of choice—from the datacenter to edge locations and remote offices—with Azure Stack. Build, deploy, and run hybrid and edge computing apps consistently across your IT ecosystem, with flexibility for diverse workloads. The Azure Stack Hub architecture lets you provide Azure services at the edge for remote locations or intermittent connectivity, disconnected from the internet. You can create hybrid solutions that process data locally in Azure Stack Hub and then aggregate it in Azure for additional processing and analytics  [https://www.nextplatform.com/2017/09/22/azure-stack-finally-takes-microsoft-public-cloud-private/#:~:text=Microsoft's%20SharePoint%2C%20Exchange%2C%20and%20Office,commerce%2C%20and%20software%20development%20products. Azure Stack Finally Takes Microsoft Public Cloud Private | Paul Teich - The Next Platform]
  
−
Differential privacy is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. Another way to describe differential privacy is as a constraint on the algorithms used to publish aggregate information about a statistical database which limits the disclosure of private information of records whose information is in the database. For example, differentially private algorithms are used by some government agencies to publish demographic information or other statistical aggregates while ensuring confidentiality of survey responses, and by companies to collect information about user behavior while controlling what is visible even to internal analysts. Roughly, an algorithm is differentially private if an observer seeing its output cannot tell if a particular individual's information was used in the computation. Differential privacy is often discussed in the context of identifying individuals whose information may be in a database. Although it does not directly refer to identification and reidentification attacks, differentially private algorithms probably resist such attacks.  Differential privacy was developed by cryptographers and thus is often associated with cryptography, and draws much of its language from cryptography. [http://en.wikipedia.org/wiki/Differential_privacy Wikipedia]
+
<blockquote style="border: 2px solid #666; padding: 10px; background-color: #ccc;"> Sometimes, this kind of environment is also referred to as a 'submarine' scenario. - [[Microsoft]]</blockquote>
  
−
=== Local Differential Privacy ===
+
In 2021, the [[Defense|DoD]] CIO designated the Department of Navy CIO as the executive agent to lead a cross-service joint working group focused on Denied-Disconnected, Intermittent, and Low bandwidth (D-DIL)... Network server software and hardware exist at the tactical edge to provide critical IT services and data in these DDIL environments, along with a variety of spectrum [[Agents#Communication | communication]]s and unclassified & classified network transports leveraging satellite links and low-Earth Orbit (LEO), Wi-Fi, cellular/4G LTE, millimeter wave/5G and others. The working group has teamed up with industry to refine DoD-unique requirements and use cases, resulting in the development of standardized architectures and solutions for the relevant collaboration and productivity tools (email, chat, voice and video, file management). These tools operate as a hybrid capability, which will allow users access to the full feature set when cloud connectivity is available, but remain productive locally within the DDIL environment. [https://www.doncio.navy.mil/chips/ArticleDetails.aspx?ID=15161 DoD working with Industry to Adapt Cloud Tools for the Tactical Edge - DON CIO]
−
[http://www.youtube.com/results?search_query=Local+Differential+Privacy+machine+learning+ML Youtube search...]
 
−
[http://www.google.com/search?q=Local+Differential+Privacy+machine+learning+ML ...Google search]
 
  
−
When the input parties do not have enough information to train a ML model, it might be better to utilize approaches that rely on local differential privacy (LDP). With LDP, each input party would perturb their data, and only release this obscure view of the data. An old, and well-known version of local privacy is randomized response (Warner 1965), which provided plausible deniability for respondents to sensitive queries. For example, a respondent would flip a fair coin: (a) if “tails”, the respondent answers truthfully, and (b) if “heads”, then flip a second coin, and respond “Yes” if heads, and “No” if tails.  RAPPOR 22 is a technology for crowdsourcing statistics from end-user client software by applying RR to Bloom filters with strong 𝜀-DP guarantees. RAPPOR is deployed in Google Chrome web browser, and it permits collecting statistics on client-side values and strings, such as their categories, frequencies, and histograms. By performing RR twice with a memoization step in between, privacy protection is maintained even when multiple responses are collected from the same participant over time. A ML oriented work, AnonML23, utilized the ideas of RR for generating histograms from multiple input parties. AnonML utilizes these histograms to generate synthetic data on which a ML model can be trained. Like other local DP approaches, AnonML is a good option when no input party has enough data to build a ML model on their own (and there is no trusted aggregator). [http://arxiv.org/ftp/arxiv/papers/1804/1804.11238.pdf Privacy Preserving Machine Learning: Threats and Solutions | Mohammad Al-Rubaie - Iowa State University]
+
Additionally, as [[Defense|DoD]] enterprise IT moves to the cloud, tactical networks must unify access to data and applications from the enterprise level to the tactical edge. This means deploying cloudlike services at the tactical edge of the network, so that data is available at the edge even when WAN connectivity is unavailable.[https://www.army.mil/article/216031/four_future_trends_in_tactical_network_modernization Four future trends In tactical network modernization | US Army]
  
−
=== Dimensionality Reduction (DR) ===
+
{|<!-- T -->
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[http://www.youtube.com/results?search_query=Dimensionality+Reduction+DR+machine+learning+ML Youtube search...]
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| valign="top" |
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[http://www.google.com/search?q=Dimensionality+Reduction+DR+machine+learning+ML ...Google search]
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{| class="wikitable" style="width: 550px;"
 +
||
 +
<youtube>3PZq7SaVPGU&t=158s</youtube>
 +
<b>Azure Stack for hybrid compute and disconnected scenarios
 +
</b><br>Latest updates to hybrid compute using the power of [[Microsoft]]'s Azure cloud on-premises with Azure Stack. Partner Director Program Manager, Natalia Mackevicius will show you how to run hybrid apps and process data between Azure Stack and your data center and in the Azure public cloud to meet your regulatory and policy requirements. Plus, see how you can use Azure Stack as a powerful control plane for disconnected scenarios to harness insights from your IoT devices running on the Edge.  
 +
|}
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|<!-- M -->
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| valign="top" |
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{| class="wikitable" style="width: 550px;"
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||
 +
<youtube>YODnLnvQXu8</youtube>
 +
<b>Introduction to [[Microsoft]] 365 | Windows, EMS, Azure, Azure Stack | Versions and Deployment Models
 +
</b><br>Azure Stack is an Azure addon that may be used in datacenter and hybrid environments. This lets you execute apps on the same platform on the edge, which [[Microsoft]] calls hybrid cloud and fully disconnected solutions. It lets you operate cloud applications that connect to on-premises data for regulatory needs while using a single DevOps and maintenance stack. So developers can code for Azure and IT pros can manage it. They have a single platform to manage cloud and on-premises data. Office 365 and Azure both run in the public cloud, whereas private and hybrid clouds employ Azure Stack.
 +
|}
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|}<!-- B -->
  
−
perturbs the data by projecting it to a lower dimensional hyperplane. Such transformation is lossy, and it was suggested by Liu et al.24 that it would enhance the privacy, since retrieving the exact original data from a reduced dimension version would not be possible (the possible solutions are infinite as the number of equations is less than the number of unknowns). Hence, Liu et al.24 proposed to use a random matrix to reduce the dimensions of the input data. Since a random matrix might decrease the utility, other approaches used both unsupervised and supervised DR techniques such as principal component analysis (PCA), discriminant component analysis (DCA), and multidimensional scaling (MDS). These approaches try to find the best projection matrix for utility purposes, while relying on the reduced dimensionality aspect to enhance the privacy. Since an approximation of the original data can still be obtained from the reduced dimensions, some approaches, e.g. Jiang et al.25, combined dimensionality reduction with DP to achieve differentially-private data publishing. While some entities might seek total hiding of their data, DR has another benefit for privacy. For datasets that have samples with two labels: a utility label and a privacy label, Kung26 proposes a DR method to enable the data owner to project her data in a way that enables maximizing the accuracy of learning for the utility labels, while decreasing the accuracy for learning the privacy labels. [http://arxiv.org/ftp/arxiv/papers/1804/1804.11238.pdf Privacy Preserving Machine Learning: Threats and Solutions | Mohammad Al-Rubaie - Iowa State University]
+
{|<!-- T -->
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| valign="top" |
 +
{| class="wikitable" style="width: 550px;"
 +
||
 +
<youtube>OHg9vcREFz4</youtube>
 +
<b>Can I Use Office 365 Offline?
 +
</b><br>Office 365 is [[Microsoft]]'s suite of cloud services for office productivity, so you may be thinking it can only be used online. However, Office 365 comes with the offline capability to make sure you can stay productive at any time, whether or not you’re connected to the internet.
 +
|}
 +
|<!-- M -->
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| valign="top" |
 +
{| class="wikitable" style="width: 550px;"
 +
||
 +
<youtube>aAZ4QXV7Wy0</youtube>
 +
<b>Azure AD Connect Sync and Cloud Sync, What’s the Difference?
 +
</b><br>Many organizations use Azure AD Connect Sync to synchronize hybrid identities from Windows AD to Azure AD DS. Microsoft recently announces a new service, Azure AD Connect Cloud Sync, that also synchronizes Windows AD identities to Azure AD. In this video, we go over how they are similar, features that are different, and when to use one or the other.
 +
|}
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|}<!-- B -->

Latest revision as of 16:56, 28 April 2024

YouTube ... Quora ...Google search ...Google News ...Bing News


Centralized vs. Decentralized vs. Distributed

Youtube search... ...Google search

Centralised-decentralised-distributed.png


Federated

Youtube search... ...Google search




Distributed

Youtube search... ...Google search

Distributed machine learning refers to multi-node machine learning algorithms and systems that are designed to improve performance, increase accuracy, and scale to larger input data sizes. Increasing the input data size for many algorithms can significantly reduce the learning error and can often be more effective than using more complex methods [8]. Distributed machine learning allows companies, researchers, and individuals to make informed decisions and draw meaningful conclusions from large amounts of data. Many systems exist for performing machine learning tasks in a distributed environment. These systems fall into three primary categories: database, general, and purpose-built systems. Each type of system has distinct advantages and disadvantages, but all are used in practice depending upon individual use cases, performance requirements, input data sizes, and the amount of implementation effort. | SpringerLink

Distinguished Lecturer : Eric Xing - Strategies & Principles for Distributed Machine Learning
The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters that promise adequate capacity to digest massive datasets and offer powerful Predictive Analytics (such as high-dimensional latent features, intermediate representations, and decision functions) thereupon. In order to run ML algorithms at such scales, on a distributed cluster with 10s to 1000s of machines, it is often the case that significant engineering efforts are required --- and one might fairly ask if such engineering truly falls within the domain of ML research or not. Taking the view that Big ML systems can indeed benefit greatly from ML-rooted statistical and algorithmic insights --- and that ML researchers should therefore not shy away from such systems design --- we discuss a series of principles and strategies distilled from our resent effort on industrial-scale ML solutions that involve a continuum from application, to engineering, and to theoretical research and development of Big ML system and architecture, on how to make them efficient, general, and with convergence and scaling guarantees. These principles concern four key questions which traditionally receive little attention in ML research: How to distribute an ML program over a cluster? How to bridge ML computation with inter-machine communication? How to perform such communication? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typical in traditional computer programs, and by dissecting successful cases of how we harness these principles to design both high-performance distributed ML software and general-purpose ML framework, we present opportunities for ML researchers and practitioners to further shape and grow the area that lies between ML and systems. This is joint work with the CMU Petuum Team.

Distributed TensorFlow training (Google I/O '18)
To efficiently train machine learning models, you will often need to scale your training to multiple GPUs, or even multiple machines. TensorFlow now offers rich functionality to achieve this with just a few lines of code. Join this session to learn how to set this up.

Distribution Strategy API: https://goo.gl/F9vXqQ https://goo.gl/Zq2xvJ

ResNet50 Model Garden example with MirroredStrategy API: https://goo.gl/3UWhj8

Performance Guides: https://goo.gl/doqGE7 https://goo.gl/NCnrCn

Commands to set up a GCE instance and run distributed training: https://goo.gl/xzwN4C

Multi-machine distributed training with train_and_evaluate: https://goo.gl/kyikAC

Watch more TensorFlow sessions from I/O '18 here → https://goo.gl/GaAnBR See all the sessions from Google I/O '18 here → https://goo.gl/q1Tr8x

Subscribe to the TensorFlow channel → https://goo.gl/ht3WGe

Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
In this video from 2018 Swiss HPC Conference, Torsten Hoefler from (ETH) Zürich presents: Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis. "Deep Neural Networks (DNN) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this talk, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. Specifically, we present trends in DNN architectures and the resulting implications on parallelization strategies. We discuss the different types of concurrency in DNNs; synchronous and asynchronous stochastic gradient descent; distributed system architectures; communication schemes; and performance modeling. Based on these approaches, we extrapolate potential directions for parallelism in deep learning." Learn more: https://hpcadvisorycouncil.com Sign up for our insideHPC Newsletter: https://insidehpc.com/newsletter

Machine Learning Systems for Highly Distributed and Rapidly Growing Data
Microsoft Research The usability and practicality of machine learning are largely influenced by two critical factors: low latency and low cost. However, achieving low latency and low cost is very challenging when machine learning depends on real-world data that are rapidly growing and highly distributed (e.g., training a face recognition model using pictures stored across many data centers globally). In this talk, I will present my work on building low-latency and low-cost machine learning systems that enable efficient processing of real-world, large-scale data. I will describe a system-level approach that is inspired by the general characteristics of machine learning algorithms, machine learning model structures, and machine learning training/serving data. In line with this approach, I will first present a system that provides both low-latency and low-cost machine learning serving (inferencing) over large-scale continuously-growing datasets (e.g. videos). Shifting the focus to model training, I will then present a system that makes machine learning training over geo-distributed datasets as fast as training within a single data center. Finally, I will discuss our ongoing efforts to tackle a fundamental and largely overlooked problem: machine learning training over skewed data partitions (e.g., facial images collected by cameras in different countries).

Peer-to-Peer

Youtube search... ...Google search



Introduction to Decentralized P2P Apps
Most people think peer-to-peer (P2P) networks are just for file sharing, but it turns out you can also build other types of applications on P2P networks with advantages like enhanced privacy and security. We’ll walk through the process of building an increasingly complex P2P cloud storage system (think Dropbox), and touch on the challenges you’d run into and some of their possible solutions. Topics include efficiently locating data within a large network and building a system where we can trust random people on the internet with our personal files. EVENT: SFNode Meetup July 2018 SPEAKER: Dylan Barnard PERMISSIONS: SFNode Meetup Organizer provided Coding Tech with the permission to republish this video.

What is a Peer to Peer Network? Blockchain P2P Networks Explained
A peer to peer network, often referred to as p2p network, is one of the key aspects of blockchain technology. In this video, we break down the complexity of peer to peer networks by first defining what a network is and how p2p networks differ from traditional networks. Learn more about P2P Networks

Proxy

Youtube search... ...Google search

Proxy vs. Reverse Proxy (Explained by Example)
Hussein Nasser In this episode we explain the difference between a Proxy (Forward proxy) and Reverse Proxy by example, and list all the benefits of each server. Online diagram tool used in this video: Http://www.gliffy.com

Proxy vs. Peer-to-Peer (P2P) Connections | remote.it webinar
In this webinar, we will explain how each connection type works, and in what applications you may prefer to use one or the other. You will learn how to use remote.it on Windows or macOS and on mobile (iOS/Android) apps to make P2P connections while we present the advantages of P2P versus traditional proxy connections from the remote.it web portal. All while making port forwardless connections.

Submarine Scenario

Youtube search... ...Google search

Microsoft’s SharePoint, Exchange, and Office 365 products run on Azure and Azure Stack, as do Microsoft’s database, e-commerce, and software development products. Extend Azure services and capabilities to your environment of choice—from the datacenter to edge locations and remote offices—with Azure Stack. Build, deploy, and run hybrid and edge computing apps consistently across your IT ecosystem, with flexibility for diverse workloads. The Azure Stack Hub architecture lets you provide Azure services at the edge for remote locations or intermittent connectivity, disconnected from the internet. You can create hybrid solutions that process data locally in Azure Stack Hub and then aggregate it in Azure for additional processing and analytics Azure Stack Finally Takes Microsoft Public Cloud Private | Paul Teich - The Next Platform

Sometimes, this kind of environment is also referred to as a 'submarine' scenario. - Microsoft

In 2021, the DoD CIO designated the Department of Navy CIO as the executive agent to lead a cross-service joint working group focused on Denied-Disconnected, Intermittent, and Low bandwidth (D-DIL)... Network server software and hardware exist at the tactical edge to provide critical IT services and data in these DDIL environments, along with a variety of spectrum communications and unclassified & classified network transports leveraging satellite links and low-Earth Orbit (LEO), Wi-Fi, cellular/4G LTE, millimeter wave/5G and others. The working group has teamed up with industry to refine DoD-unique requirements and use cases, resulting in the development of standardized architectures and solutions for the relevant collaboration and productivity tools (email, chat, voice and video, file management). These tools operate as a hybrid capability, which will allow users access to the full feature set when cloud connectivity is available, but remain productive locally within the DDIL environment. DoD working with Industry to Adapt Cloud Tools for the Tactical Edge - DON CIO

Additionally, as DoD enterprise IT moves to the cloud, tactical networks must unify access to data and applications from the enterprise level to the tactical edge. This means deploying cloudlike services at the tactical edge of the network, so that data is available at the edge even when WAN connectivity is unavailable.Four future trends In tactical network modernization | US Army

Azure Stack for hybrid compute and disconnected scenarios
Latest updates to hybrid compute using the power of Microsoft's Azure cloud on-premises with Azure Stack. Partner Director Program Manager, Natalia Mackevicius will show you how to run hybrid apps and process data between Azure Stack and your data center and in the Azure public cloud to meet your regulatory and policy requirements. Plus, see how you can use Azure Stack as a powerful control plane for disconnected scenarios to harness insights from your IoT devices running on the Edge.

Introduction to Microsoft 365 | Windows, EMS, Azure, Azure Stack | Versions and Deployment Models
Azure Stack is an Azure addon that may be used in datacenter and hybrid environments. This lets you execute apps on the same platform on the edge, which Microsoft calls hybrid cloud and fully disconnected solutions. It lets you operate cloud applications that connect to on-premises data for regulatory needs while using a single DevOps and maintenance stack. So developers can code for Azure and IT pros can manage it. They have a single platform to manage cloud and on-premises data. Office 365 and Azure both run in the public cloud, whereas private and hybrid clouds employ Azure Stack.

Can I Use Office 365 Offline?
Office 365 is Microsoft's suite of cloud services for office productivity, so you may be thinking it can only be used online. However, Office 365 comes with the offline capability to make sure you can stay productive at any time, whether or not you’re connected to the internet.

Azure AD Connect Sync and Cloud Sync, What’s the Difference?
Many organizations use Azure AD Connect Sync to synchronize hybrid identities from Windows AD to Azure AD DS. Microsoft recently announces a new service, Azure AD Connect Cloud Sync, that also synchronizes Windows AD identities to Azure AD. In this video, we go over how they are similar, features that are different, and when to use one or the other.

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