Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression

Summary

Researchers propose a federated learning scheme that combines homomorphic encryption with Johnson-Lindenstrauss dimension compression and a dual-server architecture, defending against Byzantine clients making up to 40% of the network. Compression cuts cryptographic operations from O(dn) to O(kn), where k is much smaller than d, reducing computational overhead 25 to 35 times and communication overhead 17 times relative to the non-compression version, while keeping accuracy comparable to non-private FL.

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