  Due to the distributed characteristics of Federated Learning (FL), the
vulnerability of global model and coordination of devices are the main
obstacle. As a promising solution of decentralization, scalability and
security, leveraging blockchain in FL has attracted much attention in recent
years. However, the traditional consensus mechanisms designed for blockchain
like Proof of Work (PoW) would cause extreme resource consumption, which
reduces the efficiency of FL greatly, especially when the participating devices
are wireless and resource-limited. In order to address device asynchrony and
anomaly detection in FL while avoiding the extra resource consumption caused by
blockchain, this paper introduces a framework for empowering FL using Direct
Acyclic Graph (DAG)-based blockchain systematically (DAG-FL). Accordingly,
DAG-FL is first introduced from a three-layer architecture in details, and then
two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on
different nodes to elaborate the operation of DAG-FL consensus mechanism. After
that, a Poisson process model is formulated to discuss that how to set
deployment parameters to maintain DAG-FL stably in different federated learning
tasks. The extensive simulations and experiments show that DAG-FL can achieve
better performance in terms of training efficiency and model accuracy compared
with the typical existing on-device federated learning systems as the
benchmarks.
