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

Yajing Xu, Zhihui Lu, Keke Gai, Qiang Duan, Junxiong Lin, Jie Wu, Kim Kwang Raymond Choo

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)6561-6573
Number of pages13
JournalIEEE Internet of Things Journal
Issue number8
StatePublished - Apr 15 2023

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


Dive into the research topics of 'BESIFL: Blockchain-Empowered Secure and Incentive Federated Learning Paradigm in IoT'. Together they form a unique fingerprint.

Cite this