A Byzantine-Robust Secure Federated Learning Scheme in Heterogeneous Data

Summary

Researchers propose SFLBR, a Byzantine-robust secure federated learning framework. Encryption keeps local gradients in ciphertext, which prevents the server from telling honest gradients from malicious ones, so an attacker can inject manipulated gradients into secure aggregation. SFLBR uses the gradient median as a benchmark, a layer-wise cosine similarity metric, and a trust score function, and the authors report state-of-the-art defense performance across diverse attack scenarios in experiments.

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