TY - JOUR
T1 - BESIFL
T2 - Blockchain-Empowered Secure and Incentive Federated Learning Paradigm in IoT
AU - Xu, Yajing
AU - Lu, Zhihui
AU - Gai, Keke
AU - Duan, Qiang
AU - Lin, Junxiong
AU - Wu, Jie
AU - Choo, Kim Kwang Raymond
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB1405000; in part by the National Natural Science Foundation of China under Grant 92046024, Grant 61873309, and Grant 61972034; in part by the Natural Science Foundation of Shandong Province Grant ZR2019ZD10 and Grant ZR2020ZD01; and in part by the Shanghai Science and Technology Innovation Action Plan Project under Grant 19510710500 and Grant 18510732000. The work of Kim-Kwang Raymond Choo was supported by the Cloud Technology Endowed Professorship.
Publisher Copyright:
© 2014 IEEE.
PY - 2023/4/15
Y1 - 2023/4/15
N2 - Federated learning (FL) offers a promising approach to efficient machine learning with privacy protection in distributed environments, such as Internet of Things (IoT) and mobile-edge computing (MEC). The effectiveness of FL relies on a group of participant nodes that contribute their data and computing capacities to the collaborative training of a global model. Therefore, preventing malicious nodes from adversely affecting the model training while incentivizing credible nodes to contribute to the learning process plays a crucial role in enhancing FL security and performance. Seeking to contribute to the literature, we propose a blockchain-empowered secure and incentive FL (BESIFL) paradigm in this article. Specifically, BESIFL leverages blockchain to achieve a fully decentralized FL system, where effective mechanisms for malicious node detections and incentive management are fully integrated in a unified framework. The experimental results show that the proposed BESIFL is effective in improving FL performance through its protection against malicious nodes, incentive management, and selection of credible nodes.
AB - Federated learning (FL) offers a promising approach to efficient machine learning with privacy protection in distributed environments, such as Internet of Things (IoT) and mobile-edge computing (MEC). The effectiveness of FL relies on a group of participant nodes that contribute their data and computing capacities to the collaborative training of a global model. Therefore, preventing malicious nodes from adversely affecting the model training while incentivizing credible nodes to contribute to the learning process plays a crucial role in enhancing FL security and performance. Seeking to contribute to the literature, we propose a blockchain-empowered secure and incentive FL (BESIFL) paradigm in this article. Specifically, BESIFL leverages blockchain to achieve a fully decentralized FL system, where effective mechanisms for malicious node detections and incentive management are fully integrated in a unified framework. The experimental results show that the proposed BESIFL is effective in improving FL performance through its protection against malicious nodes, incentive management, and selection of credible nodes.
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U2 - 10.1109/JIOT.2021.3138693
DO - 10.1109/JIOT.2021.3138693
M3 - Article
AN - SCOPUS:85122312817
SN - 2327-4662
VL - 10
SP - 6561
EP - 6573
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 8
ER -