FedCov: Enhanced Trustworthy Federated Learning for Machine RUL Prediction With Continuous-to-Discrete Conversion

Numerous approaches have been proposed for predicting machine remaining useful life (RUL), which helps prevent unnecessary downtime and reduces the maintenance cost in industrial systems. Most existing RUL methods rely on centralized learning and require large-scale datasets with manual labels, which are infeasible to collect. As a decentralized learning paradigm, federated learning (FL) has recently been integrated into these approaches, which aims to utilize the distributed data from local users for model training, while preserving their data privacy. However, data heterogeneity in the industry poses a critical challenge for FL, leading to model drifting issue and degraded global model performance. A straightforward method to tackle this problem is to estimate the data distribution of clients. However, it is difficult to apply this method to a regression task, since the prediction space is continuous, which increases the difficulty in estimating the data distribution. Motivated by the digital-to-analog converter in electronics, we propose a novel approach called FedCov, which involves a converter module that transforms continuous RUL values into discrete categories. Subsequently, a generator is trained to aggregate user information based on the discrete label distribution, and it is broadcasted to users as a data enhancement tool to address the data heterogeneity problem. Furthermore, to improve the performance and reliability of our FedCov, an uncertainty estimation module is proposed, which utilizes the confidence level of model predictions to adjust the training direction. Extensive experiments are conducted on the C-MAPSS benchmark, which demonstrates that our proposed FedCov effectively solves the model drift issues and improves the performances on the RUL task, achieving state-of-the-arts performances.