摘要
This article investigates a distributed online learning problem with privacy preservation, in which the learning nodes in a distributed network aims to minimize the sum of local loss functions over time horizon (Formula presented.). Based on the push-sum protocol and the Laplace mechanism, we propose a differentially private distributed dual averaging algorithm for constrained distributed online learning problem over time-varying digraphs. It is shown that the expectation of the regret of our algorithm achieves a sublinear rate of (Formula presented.). Furthermore, we provide an analysis of differential privacy, which reveals a tradeoff between the accuracy and the privacy level of our algorithm. Finally, numerical examples are presented to validate the effectiveness of the algorithm.
源语言 | 英语 |
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页(从-至) | 2485-2499 |
页数 | 15 |
期刊 | International Journal of Robust and Nonlinear Control |
卷 | 32 |
期 | 5 |
DOI | |
出版状态 | 已出版 - 25 3月 2022 |