Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy

Transfer learning can effectively solve the target task identification problem with the prerequisite of sharing all user data and target data, and has become one of the most popular algorithms in fault diagnosis. However, due to industry competition, privacy security and other factors, transfer learning methods often cannot directly deal with fault diagnosis problems under data privacy. Therefore, a federated multi-source domain adaptation method combining transfer learning and federated learning is proposed for machinery fault diagnosis with data privacy. The proposed method can comprehensively utilize all user data to achieve accurate identification of target data under the premise of data privacy protection. Specifically, a federated feature alignment idea is developed to minimize the difference in feature distribution between different client data and central server data, which can reduce the negative transfer phenomenon in the feature alignment process. Furthermore, a joint voting scheme is designed to fine-tune the global model with the help of pseudo-labeled samples to obtain more accurate fault diagnosis results. Massive experiments suggest that the proposed federated learning method has bright application prospects.