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arXiv Machine Learning

SWB-DM: A Calibrated Sliced-Wasserstein-Barycenter Aggregator with Delayed-Momentum Caching for Byzantine-Robust Federated Learning under Partial Participation

The paper introduces SWB-DM, a Byzantine‑robust federated learning aggregator that treats each slice of a client update as a one‑dimensional distribution, computes a trimmed Wasserstein barycenter across clients, and uses a medoid‑based gauge‑fixing step to recover coordinate identity. It further incorporates delayed‑momentum caching to decouple robustness from the specific clients sampled each round. Extensive experiments on CIFAR‑10, CIFAR‑100, FEMNIST, and a 500‑client scalability run reveal distinct failure modes of existing defenses and demonstrate that SWB‑DM achieves significant gains, especially when compared under equal round budgets.

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