Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics

2026-04-21

AI Summary

This study addresses the limitation of existing federated prognostic models, which typically assume homogeneous degradation processes across industrial equipment and thus struggle to handle the heterogeneous degradation commonly observed in real-world scenarios. To overcome this challenge, the authors propose a heterogeneity-aware personalized federated learning framework that identifies clients exhibiting similar degradation patterns and facilitates pairwise collaboration among them, enabling accurate remaining useful life prediction while preserving data privacy. The proposed method is the first to jointly achieve heterogeneous degradation modeling, personalized model customization, and full failure time distribution estimation within a federated setting, employing a decentralized parameter optimization algorithm based on proximal gradient descent. Experimental results on both synthetic data and the NASA turbofan engine dataset demonstrate that the approach significantly outperforms current federated prognostic models.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data local and confidential. However, traditional federated models often assume homogeneity in the degradation processes across clients, an assumption that may not hold in many industrial settings. To overcome this, this paper proposes a personalized federated prognostic model designed to accommodate clients with heterogeneous degradation processes, allowing them to build tailored prognostic models. The prognostic model iteratively facilitates the underlying pairwise collaborations between clients with similar degradation patterns, which enhances the performance of personalized federated learning. To estimate parameters jointly using decentralized datasets, we develop a federated parameter estimation algorithm based on proximal gradient descent. The proposed approach addresses the limitations of existing federated prognostic models by simultaneously achieving model personalization, preserving data privacy, and providing comprehensive failure time distributions. The superiority of the proposed model is validated through extensive simulation studies and a case study using the turbofan engine degradation dataset from the NASA repository.

Problem

• federated learning
• heterogeneity
• personalization
• prognostics
• industrial predictive analytics

Innovation

• Personalized Federated Learning
• Heterogeneous Degradation
• Federated Prognostics
• Proximal Gradient Descent
• Failure Time Prediction