A representation-level divergence metric is introduced to detect atypical clients in federated learning by quantifying changes in activation-induced input-space partitions on a shared probe set.
Convergence of deep relu networks,
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Detecting Atypical Clients in Federated Learning via Representation-Level Divergence
A representation-level divergence metric is introduced to detect atypical clients in federated learning by quantifying changes in activation-induced input-space partitions on a shared probe set.