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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2111.08211 |
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Table of Contents:
- 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