Multi-level federated learning based on cloud-edge-client collaboration and outlier-tolerance for fault diagnosis

The existing federated structure protects data privacy with only a certain level of confidentiality, and it is difficult to resist the reconstruction of other clients' data by malicious participants inside the federated and the illegal manipulation by external attackers or interceptors on the shared information. Besides, the average fusion algorithm used in the cloud center is difficult to eliminate the negative impact of outliers on model updates, and it cannot handle and fuse the time delay or even packet loss that occurs in the information obtained from each local client promptly. Therefore, to make the federated learning (FL) mechanism with stronger privacy protection ability and security, while effectively avoiding the negative impact of outliers on the aggregation of model parameters. We innovatively establish multi-Level FL based on cloud-edge-client collaboration and outlier-tolerance for fault diagnosis. At first, we build a multi-level FL network framework based on the cloud-edge-client collaborative approach for restricted sharing of network parameters level by level without data communication. Then, the edge-side performs Euclidean metrics on the restricted shared model parameters uploaded to the primary edge by each client, and uses them to identify outliers to evaluate and weight them for outlier-tolerance; Then, an outlier-tolerance mechanism is designed based on a centralized Kalman filtering algorithm that is to adjust the modeling error weights adaptively; Lastly, the cloud center performs asynchronous aggregation on the model parameters uploaded asynchronously by the highest-level edge based on a sequential Kalman filtering algorithm and transmitted the optimal model parameters back along the original path. Finally, the effectiveness of the proposed method is verified on the collected dataset.