Clustered federated learning with adaptive local differential privacy on heterogeneous IoT data
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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Through adding noise to updates, differential privacy confirms that personal data cannot be derived from the final aggregated model, an important feature for handling sensitive health data in federated learning applications.
“Through adding noise to updates, differential privacy confirms that personal data cannot be derived from the final aggregated model, an important feature for handling sensitive health data in FL applications [87].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [87]
Differential privacy is broadly adopted in federated learning to protect private contributions to model updates.
“Distinctive privacy is broadly adopted in FL to protect private contributions to model updates.”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [87]
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- Authors
- He, Z., Wang, L., & Cai, Z.
- Affiliations
- Jinan University (CN)
- Georgia State University (US)
- Venue
- IEEE Internet of Things Journal
- Published
- 2024-01-01
- Volume
- 11
- Issue
- 1
- Pages
- 137-146
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4385627109
- Cited by
- 143
- References
- 48
- Citations by year
- 2026: 35 · 2025: 55 · 2024: 48 · 2023: 5
- FWCI
- 12.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- 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 › Artificial Intelligence › 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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