14 citations · 33 across the 12 of their papers we have counts for
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math.OC2023
Sharper Convergence Guarantees for Federated Learning with Partial Model Personalization
Yiming Chen, Liyuan Cao, Kun Yuan +1
Partial model personalization, which encompasses both shared and personal variables in its formulation, is a critical optimization problem in federated learning. It balances indivi…
math.OC2023
An Enhanced Gradient-Tracking Bound for Distributed Online Stochastic Convex Optimization
Sulaiman A. Alghunaim, Kun Yuan
Gradient-tracking (GT) based decentralized methods have emerged as an effective and viable alternative method to decentralized (stochastic) gradient descent (DSGD) when solving dis…
math.OC2016★ 9 cited
Decentralized Consensus Optimization with Asynchrony and Delays
Tianyu Wu, Kun Yuan, Qing Ling +2
We propose an asynchronous, decentralized algorithm for consensus optimization. The algorithm runs over a network in which the agents communicate with their neighbors and perform l…