Privacy-preserving asynchronous federated learning framework in distributed IoT
Publication Type
Journal Article
Publication Date
8-2023
Abstract
To solve the data island issue in the distributed Internet of Things (IoT) without privacy leakage, privacy-preserving federated learning (PPFL) has been extensively explored in both academic and industrial fields. However, existing PPFL solutions still suffer from a single point of failure and incur untrusted aggregation results caused by a malicious central server, and even cause a loss of model accuracy in an asynchronous setting. To solve these issues, we propose a privacy-preserving asynchronous federated learning scheme by using blockchain. Specifically, we use blockchain to address single points of failure and untrustworthy aggregation results, implement reliable model aggregation utilizing a practical byzantine fault-tolerant protocol in an asynchronous setting, and leverage differential privacy to improve system robustness. Formal security analysis and convergence analysis demonstrate that the proposed scheme is secure and robust, and extensive experiments demonstrate that our scheme can effectively ensure the accuracy of the system when compared with state-of-the-art schemes.
Keywords
Asynchronous training, blockchain, differential privacy (DP), federated learning (FL)
Discipline
Information Security
Research Areas
Cybersecurity
Publication
IEEE Internet of Things Journal
Volume
10
Issue
15
First Page
13281
Last Page
13291
ISSN
2327-4662
Identifier
10.1109/JIOT.2023.3262546
Publisher
Institute of Electrical and Electronics Engineers
Citation
YAN, Xinru; MIAO, Yinbin; LI, Xinghua; CHOO, Kim-Kwang Raymond; MENG, Xiangdong; and DENG, Robert H..
Privacy-preserving asynchronous federated learning framework in distributed IoT. (2023). IEEE Internet of Things Journal. 10, (15), 13281-13291.
Available at: https://ink.library.smu.edu.sg/sis_research/8188
Copyright Owner and License
Authors
Additional URL
https://doi.org/10.1109/JIOT.2023.3262546