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

• NVIDIA (King’s College London) uses federated learning to create medical imaging AI | Khari Johnson - VentureBeat

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