A Trusted Federated Learning Method Based on Consortium Blockchain
Federated learning (FL) has gained significant attention in distributed machine learning due to its ability to protect data privacy while enabling model training across decentralized data sources. However, traditional FL methods face challenges in ensuring trust, security, and efficiency, particular...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/1/14 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Federated learning (FL) has gained significant attention in distributed machine learning due to its ability to protect data privacy while enabling model training across decentralized data sources. However, traditional FL methods face challenges in ensuring trust, security, and efficiency, particularly in heterogeneous environments with varying computational capacities. To address these issues, we propose a blockchain-based trusted federated learning method that integrates FL with consortium blockchain technology. This method leverages computational power registration to group participants with similar resources into private chains and employs cross-chain communication with a central management chain to ensure efficient and secure model aggregation. Our approach enhances communication efficiency by optimizing the model update process across chains, and it improves security through blockchain’s inherent transparency and immutability. The use of smart contracts for participant verification, model updates, and auditing further strengthens the trustworthiness of the system. Experimental results show significant improvements in communication efficiency, model convergence speed, and security compared to traditional federated learning methods. This blockchain-based solution provides a robust framework for creating secure, efficient, and scalable federated learning environments, ensuring reliable data sharing and trustworthy model training. |
|---|---|
| ISSN: | 2078-2489 |