Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning

Understand

Federated Learning (FL) systems are gaining popularity as a solution to training Machine Learning (ML) models from large-scale user data collected on personal devices (e.g., smartphones) without their raw data leaving the device.

• At the core of FL is a network of anonymous user devices sharing training information (model parameter updates) computed locally on personal data.
• However, the type and degree to which user-specific information is encoded in the model updates is poorly understood.
• In this paper, we identify model updates encode subtle variations in which users capture and generate data.