The Alternating Direction Method of Multipliers (ADMM) is a popular and promising distributed framework for solving large-scale machine learning problems. We consider decentralized consensus-based ADMM in which nodes may only communicate with one-hop neighbors. This may cause slow convergence. https://www.animationbengal.com/
Effects of network topology on the performance of consensus and distributed learning of SVMs using ADMM
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