Difference between revisions of "Decentralized: Federated & Distributed"
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* [[Distributed]] Learning -- high performance technique | * [[Distributed]] Learning -- high performance technique | ||
| − | * [http://en.wikipedia.org/wiki/Federated_learning Federated | + | * [http://en.wikipedia.org/wiki/Federated_learning Federated Learning | Wikipedia] |
* [http://medium.com/syncedreview/federated-learning-the-future-of-distributed-machine-learning-eec95242d897 Federated Learning: The Future of Distributed Machine Learning | Mi Zhang - Medium] | * [http://medium.com/syncedreview/federated-learning-the-future-of-distributed-machine-learning-eec95242d897 Federated Learning: The Future of Distributed Machine Learning | Mi Zhang - Medium] | ||
* [http://arxiv.org/pdf/1902.04885.pdf Federated Machine Learning: Concept and Applications | Q. Yang, Y. Liu, T. Chen and Y. Tong] | * [http://arxiv.org/pdf/1902.04885.pdf Federated Machine Learning: Concept and Applications | Q. Yang, Y. Liu, T. Chen and Y. Tong] | ||
Revision as of 11:46, 28 March 2020
Youtube search... ...Google search
- Distributed Learning -- high performance technique
- Federated Learning | Wikipedia
- Federated Learning: The Future of Distributed Machine Learning | Mi Zhang - Medium
- Federated Machine Learning: Concept and Applications | Q. Yang, Y. Liu, T. Chen and Y. Tong
- BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning | A. G. Roy, S. Siddiqui, S. Pölsterl, N. Navab, and C. Wachinger
- 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
- A Beginners Guide to Federated Learning | Santanu Bhattacharya
- Watch me Build a Cybersecurity Startup
- Federated Learning Frameworks:
- OpenMined Pysyft ...GitHub
- TensorFlow Federated Learning ...* TensorFlow Federated helps train AI models on data from different locations | Kyle Wiggers - VentureBeat
- Federated AI Ecosystem (FATE) | FEDAI.org
- Clara Federated Learning | NVIDIA
Differential Privacy 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. Understanding Differential Privacy - From Intuitions behind a Theory to a Private AI Application | An Nguyen - Towards Data Science
