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Achieving Linear Convergence for Differentially Private Full-decentralized Economic Dispatch Over Directed Networks by Jinhui Hu; Guo Chen; Huaqing Li; Tingwen Huang; Liang Ran is a Computer Science article available to read on EtoBox.

What is Achieving Linear Convergence for Differentially Private Full-decentralized Economic Dispatch Over Directed Networks about?

Privacy-preserving methods play an indispensable role in protecting agents' cost functions from being disclosed during the dispatch process. In this paper, we aim at resolving Economic Dispatch Problems (EDPs) over multi-agent systems in a fully decentralized and privacy-preserving manner. Based on both row- and column-stochastic weight matrices, we first design a fully decentralized algorithm without any privacy-masking techniques, namely F-Deed, to resolve a class of structured EDPs over directed networks. We further consider an adverse case that potential attackers including both internal honest-but-curious adversaries and external eavesdroppers try to infer or steal the local cost functions of all agents to achieve their malicious goals. To protect agents' local cost functions against differential attacks, a differentially private algorithm, dubbed DPF-Deed, is developed via enhancing F-Deed with local differential privacy (LDP). To attain LDP, DPF-Deed decomposes the gradient tracker of F-Deed into two sub-trackers, with one of them invisible to any other agents, and masks a decomposed gradient with Laplace noise. Under standard assumptions, theoretical analysis validates that

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Author

Jinhui Hu; Guo Chen; Huaqing Li; Tingwen Huang; Liang Ran

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)