  Introducing blockchain into Federated Learning (FL) to build a trusted edge
computing environment for transmission and learning has attracted widespread
attention as a new decentralized learning pattern. However, traditional
consensus mechanisms and architectures of blockchain systems face significant
challenges in handling large-scale FL tasks, especially on Internet of Things
(IoT) devices, due to their substantial resource consumption, limited
transaction throughput, and complex communication requirements. To address
these challenges, this paper proposes ChainFL, a novel two-layer
blockchain-driven FL system. It splits the IoT network into multiple shards
within the subchain layer, effectively reducing the scale of information
exchange, and employs a Direct Acyclic Graph (DAG)-based mainchain as the
mainchain layer, enabling parallel and asynchronous cross-shard validation.
Furthermore, the FL procedure is customized to integrate deeply with blockchain
technology, and a modified DAG consensus mechanism is designed to mitigate
distortion caused by abnormal models. To provide a proof-of-concept
implementation and evaluation, multiple subchains based on Hyperledger Fabric
and a self-developed DAG-based mainchain are deployed. Extensive experiments
demonstrate that ChainFL significantly surpasses conventional FL systems,
showing up to a 14% improvement in training efficiency and a threefold increase
in robustness.
