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ORDERS: An Empirical Study of Norm-Rank Aggregation for Personalized Federated Learning
Personalized federated learning combines shared representations with client-specific predictors, but the contribution of a server weighting rule can be obscured by local training and evaluation choices. We study ORDERS, a configuration that combines a shared backbone, a private residual adapter and
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