{"title":"AdapLDP-FL: An Adaptive Local Differential Privacy for Federated Learning","authors":"Gaofeng Yue;Li Yan;Liuwang Kang;Chao Shen","doi":"10.1109/TMC.2025.3533090","DOIUrl":null,"url":null,"abstract":"Federated Learning (FL) is a technique that allows multiple participants to co-train machine learning models, while also enhancing privacy by avoiding the exposure of local data. However, it is important to note that despite its effectiveness, there is still a potential risk of leaking users’ private information through weight analysis during FL updates. Local Differential Privacy (LDP) is a technique used to prevent individual information leakage by adding noise to the user's model parameters. However, FL based on LDP lacks dynamic optimization and adaptation considering privacy and data utility, especially regarding noise constraints. This paper investigates FL under the scenario of noise optimization with LDP. Specifically, given a certain privacy budget, we design the adaptive LDP method via a noise scaler, which adaptively optimizes the noise size of every client. Second, we dynamically tailor the model direction after adding noise by the designed a direction matrix, to overcome the model drift problem caused by adding noises to the client model. Finally, our method achieves higher accuracy than some existing works with the same privacy level and the convergence speed is significantly improved.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5569-5583"},"PeriodicalIF":7.7000,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10851366/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Federated Learning (FL) is a technique that allows multiple participants to co-train machine learning models, while also enhancing privacy by avoiding the exposure of local data. However, it is important to note that despite its effectiveness, there is still a potential risk of leaking users’ private information through weight analysis during FL updates. Local Differential Privacy (LDP) is a technique used to prevent individual information leakage by adding noise to the user's model parameters. However, FL based on LDP lacks dynamic optimization and adaptation considering privacy and data utility, especially regarding noise constraints. This paper investigates FL under the scenario of noise optimization with LDP. Specifically, given a certain privacy budget, we design the adaptive LDP method via a noise scaler, which adaptively optimizes the noise size of every client. Second, we dynamically tailor the model direction after adding noise by the designed a direction matrix, to overcome the model drift problem caused by adding noises to the client model. Finally, our method achieves higher accuracy than some existing works with the same privacy level and the convergence speed is significantly improved.
期刊介绍:
IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.