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A Privacy-Masking Learning Algorithm for Online Distributed Optimization over Time-Varying Unbalanced Digraphs

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  • Rong Hu
  • Binru Zhang
  • Jia-Bao Liu

Abstract

This paper investigates a constrained distributed optimization problem enabled by differential privacy where the underlying network is time-changing with unbalanced digraphs. To solve such a problem, we first propose a differentially private online distributed algorithm by injecting adaptively adjustable Laplace noises. The proposed algorithm can not only protect the privacy of participants without compromising a trusted third party, but also be implemented on more general time-varying unbalanced digraphs. Under mild conditions, we then show that the proposed algorithm can achieve a sublinear expected bound of regret for general local convex objective functions. The result shows that there is a trade-off between the optimization accuracy and privacy level. Finally, numerical simulations are conducted to validate the efficiency of the proposed algorithm.

Suggested Citation

  • Rong Hu & Binru Zhang & Jia-Bao Liu, 2021. "A Privacy-Masking Learning Algorithm for Online Distributed Optimization over Time-Varying Unbalanced Digraphs," Journal of Mathematics, Hindawi, vol. 2021, pages 1-12, October.
    • Handle: RePEc:hin:jjmath:6115451
    DOI: 10.1155/2021/6115451
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