  Since the traffic conditions change over time, machine learning models that
predict traffic flows must be updated continuously and efficiently in smart
public transportation. Federated learning (FL) is a distributed machine
learning scheme that allows buses to receive model updates without waiting for
model training on the cloud. However, FL is vulnerable to poisoning or DDoS
attacks since buses travel in public. Some work introduces blockchain to
improve reliability, but the additional latency from the consensus process
reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a scheme
that reduces the latency of aggregation to improve efficiency, but the learning
performance is unstable due to unreasonably weighted local models. To address
the above challenges, this paper offers a blockchain-based asynchronous
federated learning scheme with a dynamic scaling factor (DBAFL). Specifically,
the novel committee-based consensus algorithm for blockchain improves
reliability at the lowest possible cost of time. Meanwhile, the devised dynamic
scaling factor allows AFL to assign reasonable weights to stale local models.
Extensive experiments conducted on heterogeneous devices validate outperformed
learning performance, efficiency, and reliability of DBAFL.
