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.
翻译:由于交通条件随时间而变化,预测交通流量的机器学习模式必须在智能公共交通中不断和有效地更新。联邦学习(FL)是一个分布式的机器学习计划,允许公共汽车在不等待云层示范培训的情况下接受模式更新。然而,自公共汽车公用以来,FL很容易中毒或DDoS袭击。有些工作引入了块链来提高可靠性,但共识进程的额外延缓降低了FL的效率。Asynchronoous Freed Learning(AFL)是一个降低聚合时间以提高效率为目的的延缓度的计划,但由于地方模式不合理加权,学习绩效不稳定。为了应对上述挑战,本文提供了一个基于链式不同步的联结学习计划,并带有动态缩放因素(DBAFL )。具体地说,基于委员会的新的块链协商一致算法提高了尽可能低的时间成本的可靠性。与此同时,设计动态缩放系数使AFL能够给当地模式分配合理的重量。在异式设备上进行的广泛实验,验证了DBAFLLLL的不完善学习业绩、效率和可靠性。