An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation. (arXiv:2208.07194v3 [cs.LG] UPDATED)

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 …

arxiv asynchronous federated learning public smart transportation