GCFL: Blockchain-based Efficient Federated Learning for Heterogeneous Devices
Xiang Ying, Chenxi Liu, Dengcheng Hu · 2023
Federated Learning has emerged as a promising machine learning paradigm to protect data privacy. However, the differences between heterogeneous clients and the performance bottleneck of central server limit the efficiency of FL. As a typical decentralized solution, the combination of blockchain and FL has been studied in recent years. However, the use of single-chain blockchain and traditional consensus algorithms in these studies have drawbacks such as high resource consumption, low TPS and low scalability. This paper proposes an efficient solution that combines a DAG blockchain and FL, called GCFL(Graph with Coordinator Federated Learning). GCFL introduces a new block structure that reduces data redundancy. For DAG blockchains, we proposed a two-phase tips selection consensus algorithm that can reduce resource consumption and tolerate a certain proportion of malicious nodes. Simulation experiments show that GCFL has higher stability and fast convergence time for targeted accuracy compared to traditional on-device FL systems.