BDAFL: A Blockchain-Integrated Decentralized Asynchronous Federated Learning Algorithm in Industrial Internet

With the rapid development of the industrial internet, the value of internal data is increasing. Federated learning, which can protect data privacy, is crucial in this context. However, it faces challenges such as device heterogeneity, data heterogeneity, and single point of failure in industrial internet scenarios. To address these, we propose the Blockchain-integrated Decentralized Asynchronous Federated Learning (BDAFL) algorithm. It leverages blockchain and the Raft consensus algorithm to decouple global model updates from a central server, using multiple servers to aggregate partial model parameters and mitigate single-point failure impacts. For device heterogeneity, BDAFL introduces a weighted mechanism based on dynamic waiting times and update frequencies. To handle data heterogeneity, it uses the Earth Mover’s Distance (EMD) to measure data distribution differences and adjusts local model parameter weights accordingly. Experimental results show that BDAFL improves model accuracy by 1.27% on MNIST, 0.99% on CIFAR-10 and 0.63% on a self-bulid bearing fault dataset compared to similar algorithms, and outperforms them in precision, recall, and F1 scores across all classification categories.