Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (8): 411-420.doi: 10.11896/jsjkx.250300083

• Information Security • Previous Articles    

State-decomposition Distributed Dual Averaging Algorithm for Privacy Online ConstrainedOptimization over Directed Networks

DAI Xiangguang1, HE Chenglong2, GUAN Mingyu1, ZHANG Wei1, ZHOU Yang3, LIU Jianfeng1, LYU Qingguo4   

  1. 1 Key Laboratory of Intelligent Information Processing and Control,Chongqing Three Gorges University,Chongqing 404100,China
    2 School of Mathematics and Statistics,Chongqing Three Gorges University,Chongqing 404100,China
    3 Tencent Technology Co.,Ltd.,Guangzhou 510000,China
    4 Computer Science,Chongging University,Chongqing 400715,China
  • Received:2025-03-17 Revised:2025-05-07 Online:2025-08-15 Published:2025-08-08
  • About author:DAI Xiangguang,born in 1986,Ph.D,professor.His main research interests include optimization algorithms,neural networks,clustering and pattern recognition.
    ZHANG Wei,born in 1970,Ph.D,professor.His main research interests include information security and computational intelligence.
  • Supported by:
    Chongqing Natural Science Foundation (CSTB2023NSCQ-LZX0135,CSTB2022NSCQ-MSX1627,CSTB2022NSCQ-MSX1285) and Chongqing Municipal Education Commission Science and Technology Research Project (KJZD-M202201204, KJZD-K202201205,KJQN202301230).

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Abstract: 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.

Key words: Online constrained optimization, Distributed dual averaging, State decomposition, Directed networks, Privacy protection

CLC Number: 

  • TP301
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