FL with differential privacy

Federated Learning with Differential Privacy (FL-DP) is an approach that combines the principles of FL and Differential Privacy (DP) to ensure privacy and security in distributed ML systems. FL-DP aims to protect sensitive data while enabling collaborative model training across multiple devices or entities.

The goal of FL-DP is to achieve accurate model training without compromising the privacy of individual data contributors. It addresses the challenge of preventing data leakage during the aggregation of model updates from different participants. By incorporating DP techniques, FL-DP provides strong privacy guarantees by adding noise or perturbation to the model updates or gradients before aggregating them.

There are different approaches to implementing FL-DP. One common approach involves each client training a local ML model using their own data. The client applies techniques such as clipping and noise addition to the gradients or weights of the...