Federated Classification for Multiple Blockchain Systems

As blockchain technology continues to advance, it has become increasingly utilized as a fundamental infrastructure in various industries, such as business, justice, and finance. The widespread adoption of blockchain technology has created a pressing need for effective information exchange among different institutional units within blockchain networks. Fortunately, cross-chain technology has emerged as a promising solution for enhancing information interaction among diverse blockchain units. In this study, we examined several variables and employed multiple methodologies to validate our proposed hypothesis. Using cross-chain technology, we introduce a blockchain cross-chain federated learning framework (BCFL) that facilitates the interaction and mutual verification of data and parameters across different blockchains. This approach enables federated learning without the need to collect or coordinate model weights on a central server, while also enhancing the security of the federated learning process through the consensus algorithm mechanism of blockchains. Finally, we conduct a comparative analysis of the effectiveness of BCFL compared to traditional machine learning and centralized federated learning.