SCFL: An Efficient Cross-cluster Federated Learning Framework Based on State Channels

Blockchain-based Federated learning, called BFL, has attracted widespread attention to construct trust among multiple parties and solve a single point of failure of the central server while protecting privacy. Many researches utilize cluster and cross-chain technologies to improve poor model quality and interoperability between clusters. However, those researches still suffer from 1) high communication overhead when devices of clusters locate far away, and 2) high consensus latency since devices require frequent interactions on consensus. In this paper, we propose a cross-cluster federated learning framework based on state channels, called SCFL, to split devices into multiple clusters according to locations. We also propose a cross-cluster consensus algorithm based on cross-chain and state channels to improve the security and efficiency of off-chain and inter-chain interactions. And we also propose a hierarchical clustering method to make the model adaptable to the partition scenarios where the data is non-IID. Numerical results show that SCFL can effectively solve data sparse problems and improve the system efficiency in non-IID data partitioning cases.