Feb. 15, 2024, 5:42 a.m. | Yuanyu Wan, Tong Wei, Mingli Song, Lijun Zhang

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09173v1 Announce Type: new
Abstract: We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using only local computations and communications. Previous studies have established $O(n^{5/4}\rho^{-1/2}\sqrt{T})$ and ${O}(n^{3/2}\rho^{-1}\log T)$ regret bounds for convex and strongly convex functions respectively, where $n$ is the number of local learners, $\rho<1$ is the spectral gap of the communication matrix, and $T$ is the time horizon. However, there exist large gaps …

abstract arxiv communications cs.lg decentralized functions global loss optimization set studies type

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