Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems
This work enhances the practicality of Differential Privacy in Federated Learning, balancing privacy, efficiency, and robustness for applications in centralized systems with varying server trust levels, though it is incremental as it builds on existing stochastic optimization techniques.
The paper tackles the challenge of preserving privacy in Federated Learning within centralized systems by developing methods that ensure Differential Privacy while maintaining optimal convergence rates for homogeneous and heterogeneous data distributions, achieving linear computational complexity comparable to non-private methods.
This paper addresses the challenge of preserving privacy in Federated Learning (FL) within centralized systems, focusing on both trusted and untrusted server scenarios. We analyze this setting within the Stochastic Convex Optimization (SCO) framework, and devise methods that ensure Differential Privacy (DP) while maintaining optimal convergence rates for homogeneous and heterogeneous data distributions. Our approach, based on a recent stochastic optimization technique, offers linear computational complexity, comparable to non-private FL methods, and reduced gradient obfuscation. This work enhances the practicality of DP in FL, balancing privacy, efficiency, and robustness in a variety of server trust environment.