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Resource management for blockchain enhanced federated learning in wireless edge networks
Zhen Yang1,2, Weijing Qi3,4, Lei Guo1,2
1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People's Republic of China.
Scientific Reports
|June 8, 2026
Summary
Blockchain-based federated learning (BFL) enhances wireless edge network security and privacy. This study introduces a BFL framework with client selection and resource allocation, improving accuracy and reducing delay in resource-limited environments.
Area of Science:
- Wireless communication networks
- Distributed machine learning
- Cybersecurity and privacy
Background:
- Machine learning in wireless edge networks faces security and privacy risks due to raw data transmission.
- Federated learning (FL) mitigates privacy issues by training models locally, but centralized FL has a single point of failure.
- Blockchain-based federated learning (BFL) offers enhanced reliability and security for FL in wireless edge networks.
Purpose of the Study:
- To propose a robust Blockchain-based federated learning (BFL) framework tailored for resource-constrained wireless edge networks.
- To address the challenges of computing demands and network transmission overhead in BFL systems.
- To enhance the efficiency, accuracy, and security of federated learning in wireless edge environments.
Main Methods:
- Development of a BFL framework encompassing local client training, a consensus process, and edge server aggregation.
- Design of a client selection policy to filter out low-quality clients impacting training efficiency and accuracy.
- Implementation of a joint client selection and resource allocation scheme to optimize computing and bandwidth for BFL operations.
Main Results:
- The proposed BFL framework effectively manages computing demands and network transmission overhead in wireless edge networks.
- The client selection policy successfully excluded underperforming clients, leading to improved training efficiency.
- The joint resource allocation scheme optimized the use of computing and bandwidth, enhancing overall system performance.
Conclusions:
- The developed BFL framework provides a secure, reliable, and efficient solution for machine learning in wireless edge networks.
- The proposed client selection and resource allocation strategies significantly improve BFL system accuracy and reduce operational delay.
- This research contributes to advancing secure and efficient distributed machine learning in resource-limited edge computing environments.
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