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A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization
Federated learning over decentralized networks inherently trades off the suppression of local finite-sample noise through collaboration against the bias that collaboration introduces between heterogeneous nodes. We study this tension by performing a large-dimensional analysis of Generalized Total Va
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