BESIFL: Blockchain-Empowered Secure and Incentive Federated Learning Paradigm in IoT

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Abstract
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.
Funding Information
  • National Key Research and Development Program of China (2019YFB1405000)
  • National Natural Science Foundation of China (92046024, 61873309, 61972034)
  • Natural Science Foundation of Shandong Province (ZR2019ZD10, ZR2020ZD01)
  • Shanghai Science and Technology Innovation Action Plan Project (19510710500, 18510732000)
  • Cloud Technology Endowed Professorship

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