A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning

Paper


Wu, Di, Wang, Nai, Zhang, Jiale, Zhang, Yuan, Xiang, Yong and Gao, Longxiang. 2022. "A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning." 2022 International Joint Conference on Neural Networks (IJCNN). Padua, Italy 18 - 23 Jul 2022 IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN55064.2022.9892039
Paper/Presentation Title

A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning

Presentation TypePaper
AuthorsWu, Di, Wang, Nai, Zhang, Jiale, Zhang, Yuan, Xiang, Yong and Gao, Longxiang
Journal or Proceedings TitleProceedings of 2022 International Joint Conference on Neural Networks (IJCNN)
Number of Pages8
Year2022
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Digital Object Identifier (DOI)https://doi.org/10.1109/IJCNN55064.2022.9892039
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9892039
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9891857/proceeding
Conference/Event2022 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.

KeywordsFederated learning; Blockchain; Robustness; Poisoning attack
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
4604. Cybersecurity and privacy
Public Notes

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Byline AffiliationsDeakin University
Yangzhou University, China
Southwest University, China
Qilu University of Technology, China
Shandong Computer Science Center, China
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