An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation

TL;DR

This paper proposes DBAFL, a blockchain-based asynchronous federated learning scheme with a dynamic scaling factor, enhancing reliability, efficiency, and stability for traffic flow prediction in smart public transportation.

Contribution

It introduces a novel committee-based consensus algorithm and a dynamic scaling factor to improve asynchronous federated learning in public transportation systems.

Findings

Outperforms existing methods in learning accuracy

Reduces latency and improves efficiency

Enhances reliability against attacks

Abstract

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.