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Found 2 papers, 1 papers with code
Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCA
We show that the proposed FL algorithms converge to stationary points and Karush-Kuhn-Tucker (KKT) points of the respective unconstrained and constrained nonconvex problems, respectively.
Sample-based Federated Learning via Mini-batch SSCA
In this paper, we investigate unconstrained and constrained sample-based federated optimization, respectively.
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