Robust and Efficient Federated Learning for Machinery Fault Diagnosis in Internet of Things

Recently, Internet of Things (IoT) has been increasingly integrated into the automotive sector, enabling the development of diverse applications such as the Internet of Vehicles (IoV) and intelligent connected vehicles. Leveraging IoV technologies, operational data from core vehicle components can be collected and analyzed to construct fault diagnosis models, thereby enhancing vehicle safety. However, automakers often struggle to acquire sufficient fault data to support effective model training. To address this challenge, a robust and efficient federated learning method (REFL) is constructed for machinery fault diagnosis in collaborative IoV, which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally. In the REFL, the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness. Moreover, the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios. The proposed REFL is evaluated on non-independent and identically distributed (non-IID) real-world machinery fault dataset. Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.