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cs.LG2026
Revisiting Gradient Staleness: Evaluating Distance Metrics for Asynchronous Federated Learning Aggregation
Patrick Wilhelm, Odej Kao
In asynchronous federated learning (FL), client devices send updates to a central server at varying times based on their computational speed, often using stale versions of the glob…
cs.LG2026
Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding
Patrick Wilhelm, Inese Yilmaz, Odej Kao
Training large-scale Neural Networks requires substantial computational power and energy. Federated Learning enables distributed model training across geospatially distributed data…