IoT anomaly detection is significant due to its fundamental roles of securing modern critical infrastructures. Researchers have proposed various detection methods fostered by machine learning (ML) techniques. Federated learning (FL),as a promising distributed ML paradigm,has been employed recently to improve detection performance due to its advantages of privacy-preserving and lower latency. However,existing methods still suffer from efficiency,robustness,and security challenges. To address these problems,we initially introduce a blockchain empowered decentralized FL framework for anomaly detection in IoT systems,which provides data integrity and prevents single point failure. Further,we design an improved differentially private FL based on generative adversarial nets,aiming to optimize data utility throughout the training process. To the best of our knowledge,it is the first system to employ a decentralized FL approach with privacy-preserving for IoT anomaly detection. Simulation results demonstrate the robustness and accuracy of the developed decentralized scheme.