Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions

Federated learning has been receiving increasing attention in the recent years, which improves model performance with data privacy among different clients. The intelligent fault diagnostic problems can be largely benefited from this emerging technology since the private data generally cannot leave local storage in the real industries. While promising federated learning performance has been achieved in the literature, most studies assume data from different clients are independent and identically distributed. In the real industrial scenarios, due to variations in machines and operating conditions, the data distributions are generally different across different clients, that significantly deteriorates the performance of federated learning. To address this issue, a federated transfer learning method is proposed in this article for machinery fault diagnostics. Under the condition that data from different clients cannot be communicated, prior distributions are proposed to indirectly bridge the domain gap. In this way, client-invariant features can be extracted for diagnostics while the data privacy is preserved. Experiments on two rotating machinery datasets are implemented for validation, and the results suggest the proposed method offers an effective and promising approach for federated transfer learning in fault diagnostic problems.