Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning

• URL: http://arxiv.org/abs/2506.09870v1
• Date: Wed, 11 Jun 2025 15:42:18 GMT
• Title: Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning
• Authors: Maximilian Egger, Rawad Bitar,
• Abstract summary: We propose a multi-stage method encompassing a careful co-design of verifiable secret sharing, secure aggregation, and a tailored symmetric private information retrieval scheme.<n>We evaluate the effectiveness of our scheme on a variety of attacks and show how it outperforms the previously known techniques.
• Score: 5.2086628085326065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring resilience to Byzantine clients while maintaining the privacy of the clients' data is a fundamental challenge in federated learning (FL). When the clients' data is homogeneous, suitable countermeasures were studied from an information-theoretic perspective utilizing secure aggregation techniques while ensuring robust aggregation of the clients' gradients. However, the countermeasures used fail when the clients' data is heterogeneous. Suitable pre-processing techniques, such as nearest neighbor mixing, were recently shown to enhance the performance of those countermeasures in the heterogeneous setting. Nevertheless, those pre-processing techniques cannot be applied with the introduced privacy-preserving mechanisms. We propose a multi-stage method encompassing a careful co-design of verifiable secret sharing, secure aggregation, and a tailored symmetric private information retrieval scheme to achieve information-theoretic privacy guarantees and Byzantine resilience under data heterogeneity. We evaluate the effectiveness of our scheme on a variety of attacks and show how it outperforms the previously known techniques. Since the communication overhead of secure aggregation is non-negligible, we investigate the interplay with zero-order estimation methods that reduce the communication cost in state-of-the-art FL tasks and thereby make private aggregation scalable.

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