Xuanjie Li, Yuedong Xu*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 459-481, 2024, DOI:10.32604/cmes.2023.043247
Abstract We are investigating the distributed optimization problem, where a network of nodes works together to minimize a global objective that is a finite sum of their stored local functions. Since nodes exchange optimization parameters through the wireless network, large-scale training models can create communication bottlenecks, resulting in slower training times. To address this issue, CHOCO-SGD was proposed, which allows compressing information with arbitrary precision without reducing the convergence rate for strongly convex objective functions. Nevertheless, most convex functions are not strongly convex (such as logistic regression or Lasso), which raises the question of whether this algorithm can be applied to… More >
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