  This paper presents a fully coupled blockchain-assisted federated learning
architecture that effectively eliminates single points of failure by
decentralizing both the training and aggregation tasks across all participants.
Our proposed system offers a high degree of flexibility, allowing participants
to select shared models and customize the aggregation for local needs, thereby
optimizing system performance, including accurate inference results. Notably,
the integration of blockchain technology in our work is to promote a trustless
environment, ensuring transparency and non-repudiation among participants when
abnormalities are detected. To validate the effectiveness, we conducted
real-world federated learning deployments on a private Ethereum platform, using
two different models, ranging from simple to complex neural networks. The
experimental results indicate comparable inference accuracy between centralized
and decentralized federated learning settings. Furthermore, our findings
indicate that asynchronous aggregation is a feasible option for simple learning
models. However, complex learning models require greater training model
involvement in the aggregation to achieve high model quality, instead of
asynchronous aggregation. With the implementation of asynchronous aggregation
and the flexibility to select models, participants anticipate decreased
aggregation time in each communication round, while experiencing minimal
accuracy trade-off.
