Originally published 280224
The UK Responsible Technology Adoption Unit (RTAU) and the US National Institute of Standards and Technology (NIST) have released the first joint article in a new series explaining the concept of federated learning and how it differs from traditional centralised learning methods. Federated learning involves distributing data among participating organisations and sharing model updates instead of raw data. NIST's post explores the different ways data can be partitioned among participants in federated learning systems and discusses techniques for building privacy-preserving systems. The series will continue with future posts which will describe specific techniques for each situation.
A follow-up article in their series explaining the concept of federated learning and how it differs from traditional centralised learning methods. The latest article explores the various aspects of vertical privacy-preserving federated learning systems, including bloom filters and balancing performance with leakage.
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