A taxonomy of client-level disagreements in federated learning is presented together with a multi-track resolution strategy that enforces strict exclusion via isolated update paths, shown to handle permanent, temporal, and overlapping patterns in simulations on MNIST and N-CMAPSS.
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A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
A taxonomy of client-level disagreements in federated learning is presented together with a multi-track resolution strategy that enforces strict exclusion via isolated update paths, shown to handle permanent, temporal, and overlapping patterns in simulations on MNIST and N-CMAPSS.