JS-Drift: A reproducible Jensen-Shannon divergence procedure for drift-aware client weighting in federated learning
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Federated learning lets institutions train a shared model without exchanging raw data, but standard aggregation (FedAvg) assumes client data distributions are stationary. In practice they drift over time, and aggregation that ignores this lets unstable clients... ...
