Decentralized: Federated & Distributed
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- 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
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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
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. Wikipedia
