Efficient federated learning for fault diagnosis in industrial cloud-edge computing

Federated learning is a deep learning optimization method that can solve user privacy leakage, and it has positive significance in applying industrial equipment fault diagnosis. However, edge nodes in industrial scenarios are resource-constrained, and it is challenging to meet the computational and communication resource consumption during federated training. The heterogeneity and autonomy of edge nodes will also reduce the efficiency of synchronization optimization. This paper proposes an efficient asynchronous federated learning method to solve this problem. This method allows edge nodes to select part of the model from the cloud for asynchronous updates based on local data distribution, thereby reducing the amount of calculation and communication and improving the efficiency of federated learning. Compared with the original federated learning, this method can reduce the resource requirements at the edge, reduce communication, and improve the training speed in heterogeneous edge environments. This paper uses a heterogeneous edge computing environment composed of multiple computing platforms to verify the effectiveness of the proposed method.