An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation
This addresses the problem of secure and timely model updates for traffic prediction in smart public transportation, though it appears incremental as it builds on existing asynchronous federated learning and blockchain methods.
The paper tackles the challenge of efficiently and reliably updating traffic flow prediction models in smart public transportation by proposing DBAFL, a blockchain-based asynchronous federated learning scheme with a dynamic scaling factor, which achieves improved learning performance, efficiency, and reliability as validated through experiments on heterogeneous devices.
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.