AdapLDP-FL: An Adaptive Local Differential Privacy for Federated Learning

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Gaofeng Yue;Li Yan;Liuwang Kang;Chao Shen
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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.
AdapLDP-FL:用于联邦学习的自适应局部差分隐私
联邦学习(FL)是一种允许多个参与者共同训练机器学习模型的技术,同时还通过避免本地数据的暴露来增强隐私。然而,值得注意的是,尽管它很有效,但在FL更新期间,通过权重分析仍然存在泄露用户私人信息的潜在风险。本地差分隐私(LDP)是一种通过在用户模型参数中添加噪声来防止个人信息泄漏的技术。然而,基于LDP的FL缺乏考虑隐私和数据效用的动态优化和自适应,特别是在噪声约束方面。本文研究了基于LDP的噪声优化场景下的FL。具体而言,在给定一定隐私预算的情况下,通过噪声标度器设计自适应LDP方法,自适应优化每个客户端的噪声大小。其次,利用设计的方向矩阵在加入噪声后动态调整模型方向,克服了客户端模型中加入噪声引起的模型漂移问题;最后,在相同隐私级别的情况下,我们的方法取得了更高的准确率,并且显著提高了收敛速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
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.
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