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Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPS

文献类型： 外文期刊

作者： Jiang, Shui 1 ; Wang, Xiaoding 1 ; Que, Youxiong 3 ; Lin, Hui 1 ;

作者机构： 1.Fujian Normal Univ, Coll Comp & Cyber Secur, Fuzhou 350117, Peoples R China

2.Fujian Prov Univ, Engn Res Ctr Cyber Secur & Educ Informatizat, Fuzhou 350117, Peoples R China

3.Chinese Acad Trop Agr Sci, Inst Trop Biosci & Biotechnol, Natl Key Lab Trop Crop Breeding, Sanya 572024, Peoples R China

关键词： Cyber-Physical Systems; Distributed learning; Federated learning; Differential privacy

期刊名称：JOURNAL OF SYSTEMS ARCHITECTURE （ 影响因子：4.5； 五年影响因子：4.0 ）

ISSN： 1383-7621

年卷期： 2024 年 150 卷

页码：

收录情况： SCI

摘要： In Cyber-Physical Systems (CPS), distributed learning is essential for efficiently handling complex tasks when sufficient resources are available. However, when resources are limited, traditional distributed learning struggles to complete even simple tasks and presents a risk of privacy leakage. As a promising distributed learning paradigm, federated learning only requires the client to send the trained model to the server instead of private data, thereby preserving the client's privacy to some extent. However, with the rapid development of artificial intelligence technology, attack methods such as inference attacks still cause privacy leakage for clients participating in federated learning. Moreover, due to its distributed learning nature, federated learning cannot escape the dilemma of model accuracy being constrained by resources. To address the aforementioned problems, this paper proposes a Federated local differential privacy scheme using Model Parameter Selection, named Fed -MPS, for resource -constrained CPS. Specifically, to resolve the issue of limited CPS resources, Fed -MPS adopts an update direction consistency -based parameter selection algorithm in federated learning to extract parameters that enhance model accuracy for subsequent training, thereby improving the final model accuracy and reducing communication overhead. Furthermore, Fed -MPS applies the local differential privacy mechanism to further enhance clients' privacy. By adding noise only to the chosen parameters, the privacy budget is significantly reduced while ensuring model accuracy. Through privacy analysis, we prove that the proposed Fed -MPS scheme satisfies ( e , 6 ) - DP . Additionally, convergence analysis guarantees that Fed -MPS will converge to the global optimum with a convergence ratio of O ( T 1 2 ) within T rounds of federated learning. Extensive experiments on prominent benchmark datasets Cifar10, Mnist, and FashionMNIST demonstrate that, compared with baseline schemes, the proposed Fed -MPS provides higher model accuracy for CPS under resource constraints.

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