B2DFL: Bringing butterfly to decentralized federated learning assisted with blockchain

Abstract

We propose a novel decentralized federated learning framework called B2DFL. It decomposes the aggregation process of vanilla FL into layered and serialized sub-aggregation processes and offloads the communication and computation from a single point to distributed nodes, thus addressing the single point of failure issue in centralized FL. The decentralization of B2DFL is based on the Butterfly, a distributed network topology, to organize and orchestrate the order and rules of node aggregation. Additionally, to mitigate potential risks such as dropouts or tampering, we leverage the blockchain and IPFS systems. Specifically, after each node completes its computation (including training and aggregation), it generates a hash value of the results as proof. We maintain a Tamper-evident Data Structure (TDS) on the blockchain, which records these proofs to ensure tamper-proofing and fast verification. To reduce the storage burden on the blockchain and improve throughput, we store the aggregated results on IPFS, a system that enables quick data location through hash values of data, for data backup. We also design a node replacement mechanism for quick dropout handling. We conduct a comprehensive performance evaluation and experimental results demonstrate that B2DFL presents a significant performance improvement while achieving privacy and decentralization.