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Data-Driven Laplacian-Constrained Personalized Federated Learning
Personalization addresses one of the defining challenges in federated learning: statistical heterogeneity among clients. We show that the dissimilarity between the ideal personalized models of two clients can be upper bounded by the dissimilarity of their distributions. Motivated by this, we propose
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