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Related Experiment Video

Updated: May 24, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Distributed Online Convex Optimization With Statistical Privacy.

Mingcheng Dai, Daniel W C Ho, Baoyong Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a privacy-preserving algorithm for distributed online convex optimization in multiagent systems. It ensures statistical privacy for agents while achieving competitive regret bounds, balancing privacy and performance.

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    Area of Science:

    • Distributed Systems
    • Optimization Theory
    • Information Security

    Background:

    • Multiagent systems face challenges in distributed online constrained convex optimization.
    • Passive adversaries can compromise agent privacy by corrupting data.
    • Existing methods lack robust privacy preservation in such distributed settings.

    Purpose of the Study:

    • To develop a novel algorithm for distributed online convex optimization that guarantees statistical privacy.
    • To address the challenge of a passive adversary corrupting agents and inferring private information.
    • To analyze the trade-off between expected regret and statistical privacy.

    Main Methods:

    • Integration of a correlated perturbation mechanism with globally balanced properties into distributed online (sub)gradient descent.
    • Design of the Privacy-Preserving Distributed Online Convex Optimization (PP-DOCO) algorithm.
    • Establishment of privacy bounds using Kullback-Leibler divergence (KLD).

    Main Results:

    • The PP-DOCO algorithm provides statistical privacy guarantees for uncorrupted agents.
    • Achieved expected regret of O(sqrt(K)) for convex functions and O(log(K)) for strongly convex functions.
    • Demonstrated a trade-off between expected regret and statistical privacy, with performance matching state-of-the-art algorithms.

    Conclusions:

    • The proposed PP-DOCO algorithm effectively balances statistical privacy and expected regret in distributed online convex optimization.
    • The findings offer a significant advancement in securing multiagent systems against passive adversaries.
    • Simulation results validate the algorithm's effectiveness and the observed privacy-regret trade-off.