Optimal and Efficient Algorithms for Decentralized Online Convex Optimization

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 [abs] [ pdf ][ bib ] &copy JMLR 2025. ( edit, beta )

Authors

• Yuanyu Wan School of Software Technology, Zhejiang University, Ningbo, China
• Tong Wei School of Computer Science and Engineering, Southeast University, Nanjing 210096, China Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China
• Bo Xue City University of Hong Kong, Hong Kong, China The City University of Hong Kong Shenzhen Research Institute, Shenzhen, China
• Mingli Song Zhejiang University
• Lijun Zhang

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