摘要
This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes.Each node is endowed with a sequence of loss functions that are time-varying and a regularization function that is fixed over time.A distributed forward-backward splitting algorithm is proposed for solving this problem and both fixed and adaptive learning rates are adopted.For both cases,we show that the regret upper bounds scale as O(VT),where T is the time horizon.In particular,those rates match the centralized counterpart.Finally,we show the effectiveness of the proposed algorithms over an online distributed regularized linear regression problem.
基金
This work was supported in part by the National Natural Science Foundation of China(Nos.62022042,62273181 and 62073166)
in part by the Fundamental Research Funds for the Central Universities(No.30919011105)
in part by the Open Project of the Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment(No.GDSC202017).
