# A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction

arXiv: https://arxiv.org/abs/2305.07854 · HTML: https://mlsanity.com/paper/2305.07854/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach](https://mlsanity.com/paper/2007.09712/) (2020)
- [A comparison of LSTM and GRU networks for learning symbolic sequences](https://mlsanity.com/paper/2107.02248/) (2021)
- [Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation](https://mlsanity.com/paper/2101.03678/) (2021)
- [A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures](https://mlsanity.com/paper/2101.03704/) (2021)
- [Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation](https://mlsanity.com/paper/2203.16373/) (2022)
- [Digital Twin for Real-time Li-ion Battery State of Health Estimation with Partially Discharged Cycling Data](https://mlsanity.com/paper/2212.04622/) (2022)

## Built on by (most cited first, 12 of 12)

- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [A Change Point Detection Integrated Remaining Useful Life Estimation Model under Variable Operating Conditions](https://mlsanity.com/paper/2401.04351/) (2024)
- [Fed-Joint: Joint Modeling of Nonlinear Degradation Signals and Failure Events for Remaining Useful Life Prediction using Federated Learning](https://mlsanity.com/paper/2503.13404/) (2025)
- [A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT](https://mlsanity.com/paper/2410.01644/) (2024)
- [A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals](https://mlsanity.com/paper/2410.11101/) (2024)
- [GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task](https://mlsanity.com/paper/2602.21873/) (2026)
- [Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments](https://mlsanity.com/paper/2505.03825/) (2025)
- [Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling](https://mlsanity.com/paper/2508.21785/) (2025)
- [Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics](https://mlsanity.com/paper/2604.19451/) (2026)
- [Collaborative System Failure Prognostics via Federated Longitudinal-Survival Modeling](https://mlsanity.com/paper/2607.26038/) (2026)
- [Robust and Personalized Federated Learning for Aircraft-Engine Prognostics under Benign and Adversarial Client Heterogeneity](https://mlsanity.com/paper/2608.04045/) (2026)
- [FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation](https://mlsanity.com/paper/2608.26433/) (2026)

## Abstract (arXiv)

Data-driven industrial health prognostics require rich training data to develop accurate and reliable predictive models. However, stringent data privacy laws and the abundance of edge industrial data necessitate decentralized data utilization. Thus, the industrial health prognostics field is well suited to significantly benefit from federated learning (FL), a decentralized and privacy-preserving learning technique. However, FL-based health prognostics tasks have hardly been investigated due to the complexities of meaningfully aggregating model parameters trained from heterogeneous data to form a high performing federated model. Specifically, data heterogeneity among edge devices, stemming from dissimilar degradation mechanisms and unequal dataset sizes, poses a critical statistical challenge for developing accurate federated models. We propose a pioneering FL-based health prognostic model with a feature similarity-matched parameter aggregation algorithm to discriminatingly learn from heterogeneous edge data. The algorithm searches across the heterogeneous locally trained models and matches neurons with probabilistically similar feature extraction functions first, before selectively averaging them to form the federated model parameters. As the algorithm only averages similar neurons, as opposed to conventional naive averaging of coordinate-wise neurons, the distinct feature extractors of local models are carried over with less dilution to the resultant federated model. Using both cyclic degradation data of Li-ion batteries and non-cyclic data of turbofan engines, we demonstrate that the proposed method yields accuracy improvements as high as 44.5% and 39.3% for state-of-health estimation and remaining useful life estimation, respectively.