Label-Aware Distribution-Based Dynamic Weighting Aggregation for Hierarchical Backdoor Defense in Federated Learning

Summary

Researchers propose LADW (Label-Aware Distribution-based Dynamic Weighting), a black-box aggregation strategy for federated learning that defends against backdoor attacks without trusted client information. It uses a small auxiliary dataset, which may be synthetic data from a large text-to-image model, to infer each client's implicit label distribution from update similarity and reweight aggregation to suppress label-concentrated, potentially malicious updates. Experiments on multiple benchmarks report reduced attack success rates and preserved main-task accuracy, with state-of-the-art results under highly non-IID conditions.

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