Federated Learning Meets Blockchain: State Channel-Based Distributed Data-Sharing Trust Supervision Mechanism

With the rapid development of the 5G and 6G technology, it has become an inevitable trend to share the cross-domain scattered data and enhance data value transmission. As a new data-sharing technology with intelligence and privacy computing, federated learning (FL) receives wide attention. It can realize data value delivery and data privacy protection at the same time, however, it lacks supervision in the application process, and the reliability of the calculation process and result transmission cannot be guaranteed. As a distributed ledger technology, blockchain has the trust property but lacks computing power. Therefore, we propose to extend the computing and supervision capabilities of blockchain with state channel, using state channel to create sandboxes and instantiate FL tasks in order to realize the trust supervision mechanism based on sandboxes. In this article, we establish an FL-based distributed data-sharing architecture and on the basis of the architecture we design a state channel-based distributed data-sharing trust supervision mechanism. Through theoretical analysis and experimental verification, the supervision mechanism we designed has an excellent performance in improving system security, resisting malicious attacks, and improving data model quality.

Automated summary

With the rapid development of 5G and 6G technology, sharing cross-domain scattered data and enhancing data value transmission has become an inevitable trend. Federated learning (FL) is a new data-sharing technology that has gained wide attention due to its intelligence and privacy computing capabilities. However, FL lacks supervision in the application process, leading to unreliable calculation processes and result transmissions. To address this, we propose extending the computing and supervision capabilities of blockchain using state channels to create sandboxes and instantiate FL tasks for a trust supervision mechanism. Through theoretical analysis and experimental verification, our designed supervision mechanism demonstrates excellent performance in improving system security, resisting malicious attacks, and enhancing data model quality.