Federated learning with network pruning and rebirth for remaining useful life prediction of engineering systems

Remaining useful life (RUL) prediction has achieved considerable success through centralized learning methods. However, traditional data aggregation may cause privacy disclosure, and existing prediction models are often too large to be trained efficiently. This paper proposes a RUL prediction method in the federated learning (FL) framework, which aims to develop a lightweight model using network pruning and rebirth strategies. First, a deep convolutional neural network (DCNN) is designed as the prediction model. Next, the Taylor expansion and the l₂ norm pruning criteria are executed on the convolutional and fully-connected layers of DCNN to prune some unimportant feature maps and neurons, respectively. After each pruning operation, the network rebirth strategies, including model relocation, federated averaging (FedAvg), and selective retraining, are used to fine-tune the pruned model in the FL. Finally, the network pruning and rebirth occur alternately to produce a compact RUL prediction model with fewer parameters, which can achieve the same good performance as the original one. Experiments study on the C-MAPSS dataset demonstrates the effectiveness of the proposed method.