The DOI matches its Crossref record; 47 of the 48 references it cites were found in a registry or an index.
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.
Overview
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In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H., Rasul, K., & Vollgraf, R. (2017) · arXiv · doi:10.48550/arxiv.1708.07747 · Cited by 6,108
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023
Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2683200 · Cited by 2,793
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077
SecureML: A System for Scalable Privacy-Preserving Machine Learning
Mohassel, P., & Zhang, Y. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.12 · Cited by 1,936
Zhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906
Towards Personalized Federated Learning
Tan, A. Z., Yu, H., Cui, L., & Yang, Q. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3160699 · Cited by 1,474
Research works printed but not linked: 2
- Xu, 2021, FedMood: Federated learning on mobile health data for mood detection, arXiv:2102.09342 · arXiv
- LeCun, 2010, MNIST Handwritten Digit Database
Cited by
Sorted by citations
Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228
Privacy-Preserving Federated Learning for Intrusion Detection in IoT Environments: A Survey
Vyas, A., Lin, P., Hwang, R., & Tripathi, M. (2024) · IEEE Access · doi:10.1109/access.2024.3454211 · Cited by 115
“The empirical evaluations in [179] demonstrate that the proposed ACS-FL (adaptive clipping, weight compression, and parameter shuffling) scheme achieves superior performance in striking an equilibrium between privacy preservation and utility maximization.”
BackgroundInfluentialMohammadi, S., Balador, A., Sinaei, S., & Flammini, F. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104918 · Cited by 93
IOFL: Intelligent-Optimization-Based Federated Learning for Non-IID Data
Li, X., Zhao, H., & Deng, W. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3354942 · Cited by 60
“Regarding privacy protection issues in FL, some existing researches typically combine FL with cryptographic techniques of differential privacy, multiparty secure computing, and homomorphic encryption, or replace client server communication content [30], [31], [32].”
BackgroundDigital Twin-Assisted Federated Learning Service Provisioning Over Mobile Edge Networks
Zhang, R., Xie, Z., Yu, D., Liang, W., & Cheng, X. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3337317 · Cited by 36
“There are many investigations on implementing FL in MEC networks [24], [25], [26], [27].”
Background
Metadata
- 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
- 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 › 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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