Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization

14 October 2022

Papers citing "Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization"

6 / 6 papers shown

Title
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement Qinglun Li Miao Zhang Mengzhu Wang Quanjun Yin Li Shen OODD FedML 21 0 0 09 Oct 2024
Structured Cooperative Learning with Graphical Model Priors Shuang-Yang Li Tianyi Zhou Xinmei Tian Dacheng Tao 30 0 0 16 Jun 2023
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression Yutong He Xinmeng Huang Yiming Chen W. Yin Kun Yuan 28 7 0 12 May 2023
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence Kun-Yen Huang Shin-Yi Pu 30 9 0 14 Jan 2023
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training Kun Yuan Yiming Chen Xinmeng Huang Yingya Zhang Pan Pan Yinghui Xu W. Yin MoE 48 60 0 24 Apr 2021
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 136 1,198 0 16 Aug 2016