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Differentially private consensus and distributed optimization in multi-agent systems: A review
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DOI：10.1016/j.neucom.2024.127986.png)
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In the past few decades, distributed multi-agent system (MAS) control has received growing attention due to its numerous advantages. Nonetheless, the substantial reliance on local information exchange in distributed MAS control has given rise to significant privacy concerns. Differential privacy (DP), a mathematically rigorous privacy notion, has gained popularity as a means of safeguarding privacy across multiple fields, including distributed MAS control. In this paper, we present an in-depth overview of the techniques for preserving DP in distributed MAS control, concentrating on consensus and distributed optimization. We begin by outlining the defining features and modeling of MASs from the control theory perspective. Then, we illustrate the motivation for adopting differentially private mechanisms to protect the privacy of distributed MAS control and present the fundamental principles of DP. Based on them, we investigate the cutting-edge techniques designed to preserve DP in consensus and distributed optimization. This review sheds light on the current landscape of DP applications in distributed MAS control and lays the groundwork for future progress in this essential field.
Keyword:
Consensus
Distributed optimization
Differential privacy
Multi-agent systems
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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