摘要
文中研究了一类具有公共约束集的分布式在线约束优化问题。网络中的各个节点通过局部计算和通信协同求解该问题,每个节点仅能访问自身的局部损失函数,该函数的取值依赖于其每次迭代中的决策变量。然而,由于节点在通信过程中不断广播与隐私相关的信息,大多数现有算法可能面临隐私泄露的风险。为此,提出了一种高效的状态分解分布式对偶平均算法。该算法结合状态分解和梯度调整策略,以增强隐私保护能力,同时消除有向网络中的不平衡性。值得注意的是,该算法无需额外的隐藏信号,也不会显著增加计算量。理论分析表明,该方法不仅能实现次线性遗憾,还能够有效保护各节点损失函数的隐私性。此外,通过仿真实验验证了该算法的收敛性和可行性。
This paper investigates a class of distributed online constrained optimization problems with a common constraint set.In this setting,nodes in the network collaborate to solve the problem through local computation and communication.Each node can only access its own local loss function,whose value depends on its decision variables at each iteration.However,since nodes continuously broadcast information related to their private data during communication,most existing algorithms face the risk of privacy leakage.To address this issue,this paper proposes an efficient state decomposition-based distributed dual averaging algorithm.This algorithm integrates state decomposition and gradient adjustment strategies to enhance privacy protection while mitigating imbalances in directed networks.Notably,it does not require additional hidden signals or significantly increase computational complexity.Theoretical analysis shows that the proposed method achieves the desired sublinear regret while effectively preserving the privacy of each node’s loss function.Furthermore,simulation experiments confirm the convergence and feasibility of the algorithm.
作者
代祥光
贺成龙
管明宇
张伟
周炀
刘建峰
吕庆国
DAI Xiangguang;HE Chenglong;GUAN Mingyu;ZHANG Wei;ZHOU Yang;LIU Jianfeng;LYU Qingguo(Key Laboratory of Intelligent Information Processing and Control,Chongqing Three Gorges University,Chongqing 404100,China;School of Mathematics and Statistics,Chongqing Three Gorges University,Chongqing 404100,China;Tencent Technology Co.,Ltd.,Guangzhou 510000,China;Computer Science,Chongging University,Chongqing 400715,China)
出处
《计算机科学》
北大核心
2025年第8期411-420,共10页
Computer Science
基金
重庆市自然科学基金(CSTB2023NSCQ-LZX0135,CSTB2022NSCQ-MSX1627,CSTB2022NSCQ-MSX1285)
重庆市教委科学技术研究项目(KJZD-M202201204,KJZD-K202201205,KJQN202301230)。
关键词
在线约束优化
分布式对偶平均算法
状态分解
有向网络
隐私保护
Online constrained optimization
Distributed dual averaging
State decomposition
Directed networks
Privacy protection
