FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning
Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather than waiting for all selected clients to finish. While efficient, this arrival-driven aggregation can silently distort representation learning under heterogeneous particip...
Prashant Bajpai, Divya Saxena, Philippe Lalanda et al.
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