ClusterGuard: Secure Clustered Aggregation for Federated Learning With Robustness

Summary

ClusterGuard is a secure clustered aggregation scheme for federated learning that aims to protect privacy and resist poisoning attacks. It uses a Verifiable Random Function for fair client clustering, a key-homomorphic masking mechanism with verifiable secret sharing for aggregation within clusters, and a dual filter based on cosine similarity and norm. Experiments on standard datasets report over 2x efficiency gains over advanced secure aggregation methods, and with 20% malicious clients the model keeps accuracy comparable to the original.

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