A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning
Paper
Paper/Presentation Title | A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning |
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Presentation Type | Paper |
Authors | Wu, Di, Wang, Nai, Zhang, Jiale, Zhang, Yuan, Xiang, Yong and Gao, Longxiang |
Journal or Proceedings Title | Proceedings of 2022 International Joint Conference on Neural Networks (IJCNN) |
Number of Pages | 8 |
Year | 2022 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Digital Object Identifier (DOI) | https://doi.org/10.1109/IJCNN55064.2022.9892039 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/9892039 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/9891857/proceeding |
Conference/Event | 2022 International Joint Conference on Neural Networks (IJCNN) |
Event Details | 2022 International Joint Conference on Neural Networks (IJCNN) Parent International Joint Conference on Neural Networks (IJCNN) Delivery In person Event Date 18 to end of 23 Jul 2022 Event Location Padua, Italy |
Abstract | With the expansion of the Internet of Things (IoT) development and application, federated learning has gained higher popularity in industrial researching fields. However, the security issues in federated learning have become hot-spots in the research area, such as privacy-preserving and poisoning attacks. This paper proposes a robust blockchained multi-layer decentralized federated learning (RBML-DFL) framework to ensure the federated learning's robustness. Firstly, by adopting the three-layered framework, the blockchain connects the federated learning components to secure the privacy and data safety of federated learning. Secondly, the proposed framework provides resilience on poisoning attacks to the central model compared to typical federated learning frameworks. Lastly, the decentralized structure associated with the blockchain tracing back mechanism can prevent the central server failure or mal-function compared to centralized federated learning. We evaluate and compare the proposed framework with other state-of-the-art federated learning frameworks on the accuracy, latency, and system robustness under poisoning attacks. The results show that the proposed RBML-DFL framework outperforms state-of-the-art baseline frameworks on all three metrics: accuracy, latency, and the robustness of the federated learning. |
Keywords | Federated learning; Blockchain; Robustness; Poisoning attack |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4602. Artificial intelligence |
4604. Cybersecurity and privacy | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions, but may be accessed online. Please see the link in the URL field. |
Byline Affiliations | Deakin University |
Yangzhou University, China | |
Southwest University, China | |
Qilu University of Technology, China | |
Shandong Computer Science Center, China |
https://research.usq.edu.au/item/z4y14/a-blockchain-based-multi-layer-decentralized-framework-for-robust-federated-learning
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