CMBA-FL: Communication-mitigated and blockchain-assisted federated learning for traffic flow predictions

As an effective strategy to address urban traffic congestion, traffic flow prediction has gained attention from Federated-Learning (FL) researchers due FL's ability to preserving data privacy. However, existing methods face challenges: some are too simplistic to capture complex traffic patterns effectively, and others are overly complex, leading to excessive communication overhead between cloud and edge devices. Moreover, the problem of single point failure limits their robustness and reliability in real-world applications. To tackle these challenges, this paper proposes a new method, CMBA-FL, a Communication-Mitigated and Blockchain-Assisted Federated Learning model. First, CMBA-FL improves the client model's ability to capture temporal traffic patterns by employing the Encoder-Decoder framework for each edge device. Second, to reduce the communication overhead during federated learning, we introduce a verification method based on parameter update consistency, avoiding unnecessary parameter updates. Third, to mitigate the risk of a single point of failure, we integrate consensus mechanisms from blockchain technology. To validate the effectiveness of CMBA-FL, we assess its performance on two widely used traffic datasets. Our experimental results show that CMBA-FL reduces prediction error by 11.46%, significantly lowers communication overhead, and improves security.