Blockchain interoperability facilitates the transmission and interaction of data across different blockchain networks, ensuring communication and data exchange. Blockchain federated learning is a method that promotes the blockchain technology to address the challenge of information silos. Participants in blockchain federated learning typically store their data on private blockchains to ensure that data is shared among specific members. However, careful consideration must be given to the issue of data privacy leakage during blockchain interoperability. Malicious participants in federated learning may illicitly access private data through various attack methods, diminishing overall reliability and credibility. In order to mitigate the risk of data sharing among untrusted clients, we propose the Federated Learning-Relay Chain (FL-R) framework. By leveraging the high scalability of relay chain technology and FL, the private set intersection of distinct blockchain data within the paradigm has been achieved, ensures that only legitimate and authorized parties can participate in the federated learning process, aiming to enhance the efficiency and security of the blockchain federated learning framework. Results analysis shows that FL-R exhibits strong stability when dealing with large amounts of data. Moreover, in the same global epoch, FL-R demonstrated a 3.7% increase in accuracy compared to FedProx, ensuring the secure and reliable transmission of data within the blockchain.
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Wu et al. (2024) studied this question.