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CRIP selectively fuses features from clients based on representational similarity, leading to superior performance in one-shot federated learning under extreme domain heterogeneity.
Unbalanced class prevalence, not just disjoint label sets, is the dominant factor hindering federated learning performance under label-space heterogeneity.
