FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning
This addresses privacy concerns for clients in federated learning, but it is incremental as it builds on existing methods like GANs and decomposition techniques.
The paper tackles the problem of privacy leakage in federated learning from gradient-based attacks, proposing FedCG, which uses conditional GANs to protect privacy while maintaining competitive model performance, with experiments showing it achieves similar accuracy to baselines.
Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data. Recent works demonstrate that information exchanged during FL is subject to gradient-based privacy attacks, and consequently, a variety of privacy-preserving methods have been adopted to thwart such attacks. However, these defensive methods either introduce orders of magnitude more computational and communication overheads (e.g., with homomorphic encryption) or incur substantial model performance losses in terms of prediction accuracy (e.g., with differential privacy). In this work, we propose $\textsc{FedCG}$, a novel federated learning method that leverages conditional generative adversarial networks to achieve high-level privacy protection while still maintaining competitive model performance. $\textsc{FedCG}$ decomposes each client's local network into a private extractor and a public classifier and keeps the extractor local to protect privacy. Instead of exposing extractors, $\textsc{FedCG}$ shares clients' generators with the server for aggregating clients' shared knowledge, aiming to enhance the performance of each client's local networks. Extensive experiments demonstrate that $\textsc{FedCG}$ can achieve competitive model performance compared with FL baselines, and privacy analysis shows that $\textsc{FedCG}$ has a high-level privacy-preserving capability. Code is available at https://github.com/yankang18/FedCG