AMBER: Robust Federated Learning Based on Client Verification

Summary

This paper analyzes Local Chained Attacks (LCAs), which span local dataset pollution, selective inputs, and training process tampering in federated learning clients. It proposes AMBER, a three-layer verification framework that uses vector commitments for dataset integrity, a local consistency check for selective input attacks, and TEE-based verification of input-output correlation. Experiments across multiple datasets, models, and attack scenarios report better defense effectiveness than existing approaches with low overhead, including in Non-IID settings.

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