  Federated learning (FL) has emerged as a promising master/slave learning
paradigm to alleviate systemic privacy risks and communication costs incurred
by cloud-centric machine learning methods. However, it is very challenging to
resist the single point of failure of the master aggregator and attacks from
malicious participants while guaranteeing model convergence speed and accuracy.
Recently, blockchain has been brought into FL systems transforming the paradigm
to a decentralized manner thus further improve the system security and learning
reliability. Unfortunately, the traditional consensus mechanism and
architecture of blockchain systems can hardly handle the large-scale FL task
due to the huge resource consumption, limited transaction throughput, and high
communication complexity. To address these issues, this paper proposes a
two-layer blockchaindriven FL framework, called as ChainsFL, which is composed
of multiple subchain networks (subchain layer) and a direct acyclic graph
(DAG)-based mainchain (mainchain layer). In ChainsFL, the subchain layer limits
the scale of each shard for a small range of information exchange, and the
mainchain layer allows each shard to share and validate the learning model in
parallel and asynchronously to improve the efficiency of 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. In order to
provide a proof-ofconcept implementation and evaluation, multiple subchains
base on Hyperledger Fabric are deployed as the subchain layer, and the
self-developed DAG-based mainchain is deployed as the mainchain layer. The
experimental results show that ChainsFL provides acceptable and sometimes
better training efficiency and stronger robustness compared with the typical
existing FL systems.
