OCFL automatically picks the clustering round by detecting a rise in the p-norm of the pairwise cosine-distance matrix of client gradients, and with density-based clustering it recovers client cohorts earlier and more accurately than prior methods.
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One-Shot Clustering for Federated Learning Under Clustering-Agnostic Assumption
OCFL automatically picks the clustering round by detecting a rise in the p-norm of the pairwise cosine-distance matrix of client gradients, and with density-based clustering it recovers client cohorts earlier and more accurately than prior methods.