Federated learning for machinery fault diagnosis with dynamic validation and self-supervision

Highlights：

• A specially designed federated learning method is proposed for machinery fault diagnosis problems.

• A self-supervised learning algorithm is proposed for better explorations of time-series machinery data.

• A dynamic validation scheme is proposed to adaptively implement model averaging operation.

• The challenging scenarios with non-independent and identically distributed user data are addressed.

• The proposed data privacy-preserving learning scheme is validated through experiments on two rotating machinery datasets.