SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression

Understand

To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient federated learning algorithms with the aid of communication compression.

• On the other end, privacy-preserving, especially at the client level, is another important desideratum that has not been addressed simultaneously in the presence of advanced communication compression techniques yet.
• In this paper, we propose a unified framework that enhances the communication efficiency of private federated learning with communication compression.
• Exploiting both general compression operators and local differential privacy, we first examine a simple algorithm that applies compression directly to differentially-private stochastic gradient descent, and identify its limitations.