Cited by 143Privacy-Preserving Technologies in DataInternet Traffic Analysis and Secure E-votingCryptography and Data Security

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Clustered Federated Learning With Adaptive Local Differential Privacy on Heterogeneous IoT Data

He, Zaobo, Wang, Lintao, Cai, Zhipeng·2024·IEEE Internet of Things Journal

DOI 10.1109/jiot.2023.3299947

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Abstract

The Internet of Things (IoT) is penetrating many aspects of our daily life with the proliferation of artificial intelligence applications. Federated learning (FL) has emerged as a promising paradigm enabling many intelligent IoT applications; however, the transmitted model gradients or weights still encode private information, which can be exploited to launch inference attacks. One popular way is to apply local differential privacy (LDP) into FL. However, existing work does not provide a practical solution due to two issues. First, the fine-grained range difference of weights in different layers of an FL model has not been explicitly considered. Second, the accumulated privacy budget may cause a budget explosion. In this article, we propose a local differentially private scheme to train clustered FL models on heterogeneous IoT data by using adaptive clipping, weight compression, and parameter shuffling (namely, ACS-FL), aimed at mitigating the curse of dimensionality, the amount of LDP noise, and the communication overhead of IoT devices. Empirical evaluations on MNIST, fashion-MNIST, and Federated Extended MNIST demonstrate that ACS-FL achieves a superior performance in balancing the tradeoff between privacy and utility.

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Authors: He, Z., Wang, L., & Cai, Z.
Affiliations: Jinan University (CN)
Georgia State University (US)
ORCID: He, Zaobo
Wang, Lintao
Cai, Zhipeng
Venue: IEEE Internet of Things Journal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2024-01-01
Volume: 11
Issue: 1
Pages: 137-146
ISSN: 2327-4662 (ISSN-L)
2372-2541
DOI: 10.1109/jiot.2023.3299947
OpenAlex: W4385627109
Cited by: 143
References: 48
Citations by year: 2026: 35 · 2025: 55 · 2024: 48
FWCI: 12.66
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Internet of Things Journal (publishedVersion)
Funders: Guangzhou Basic and Applied Basic Research Project (SL2022A04J01519)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
Keywords: federated learning, local differential privacy, Internet of Things, data heterogeneity, clustering, adaptive clipping, weight compression, privacy-utility trade-off, inference attacks, communication overhead

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Clustered Federated Learning With Adaptive Local Differential Privacy on Heterogeneous IoT Data

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