Secure and Efficient Federated Learning Through Layering and Sharding Blockchain

Introducing blockchain into Federated Learning (FL) to build a trusted edge computing environment for transmission and learning has become a new decentralized learning pattern, which has received extensive attention. However, the traditional consensus mechanism and architecture of blockchain systems can hardly handle the large-scale FL task and run on IoT devices due to the huge resource consumption, limited transaction throughput, and high communication complexity. To address these issues, this paper proposes a two-layer blockchain-driven FL system, called ChainFL, which splits the IoT network into multiple shards as the subchain layer to limit the scale of information exchange, and adopts a Direct Acyclic Graph (DAG)-based mainchain as the mainchain layer to achieve parallel and asynchronous cross-shard validation. Furthermore, the FL procedure is customized to deeply integrate with blockchain technology, and the modified DAG consensus mechanism is proposed to mitigate the distortion caused by abnormal models. To provide a proof-of-concept implementation and evaluation, multiple subchains based on Hyperledger Fabric and the self-developed DAG-based mainchain are deployed. The extensive experimental results demonstrated that our proposed ChainFL system outperforms the existing main FL systems in terms of acceptable and fast training efficiency (by up to 14%) and stronger robustness (by up to three times).