Blockchain-based privacy-preserving multi-tasks federated learning framework

Abstract

Federated learning (FL), as an effective method to solve the problem of “data island”, has become one of the hot and widespread concern topics in recent years. However, with the using of FL technology in the practical applications, an increasing number of FL tasks make the training management be more complex and the trade-off of multi-task becomes difficult. To overcome this weakness, this work proposes a privacy-preserving FL framework with multi-tasks using partitioned blockchain, which can run several different FL tasks by multiple requesters. First, a temporary committee is formed for an FL task to facilitating visualization, organization and management of security aggregation. Second, the proposed framework combines Paillier homomorphic encryption with Pearson correlation coefficient to protect users' privacy and ensure the accuracy of global model. Finally, a new blockchain-based reward method is presented to inspire participants to share their valuable data. The experimental results show that the global model accuracy of our proposed framework is able to reach 98.43$\mathrm{\%}$. Obviously, the proposed framework is more suitable for practical application environment, especially in industrial application field.

Keywords:

• Partitioned blockchain
• federated learning
• privacy-preserving
• multi-tasking

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China[62202390]Science and Technology Fund of Sichuan Province[2022NSFSC0556]Science and Technology Fund of Sichuan Province[2023YFG0306]National Natural Science Foundation of China[62177019].