Privacy protection as a major concern of the industrial big data enabling entities makes the massive safety-critical operation data of a wind turbine unable to exert its great value because of the threat of privacy leakage. How to improve the diagnostic accuracy of decentralized machines without data transfer remains an open issue; especially these machines are almost accompanied by skewed class distribution in the real industries. In this study, a class-imbalanced privacy-preserving federated learning framework for the fault diagnosis of a decentralized wind turbine is proposed. Specifically, a biometric authentication technique is first employed to ensure that only legitimate entities can access private data and defend against malicious attacks. Then, the federated learning with two privacy-enhancing techniques enables high potential privacy and security in low-trust systems. Then, a solely gradient-based self-monitor scheme is integrated to acknowledge the global imbalance information for class-imbalanced fault diagnosis. We leverage a real-world industrial wind turbine dataset to verify the effectiveness of the proposed framework. By comparison with five state-of-the-art approaches and two nonparametric tests, the superiority of the proposed framework in imbalanced classification is ascertained. An ablation study indicates that the proposed framework can maintain high diagnostic performance while enhancing privacy protection.