# Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

arXiv: https://arxiv.org/abs/2304.11101 · HTML: https://mlsanity.com/paper/2304.11101/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems](https://mlsanity.com/paper/2006.07856/) (2020)
- [Failure Prediction in Production Line Based on Federated Learning: An Empirical Study](https://mlsanity.com/paper/2101.11715/) (2021)
- [New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning](https://mlsanity.com/paper/2107.13173/) (2021)
- [Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System](https://mlsanity.com/paper/2110.08202/) (2021)
- [Minimal-Configuration Anomaly Detection for IIoT Sensors](https://mlsanity.com/paper/2110.04049/) (2021)
- [Interpretable Predictive Maintenance for Hard Drives](https://mlsanity.com/paper/2102.06509/) (2021)
- [Smart Active Sampling to enhance Quality Assurance Efficiency](https://mlsanity.com/paper/2209.11464/) (2022)

## Built on by (most cited first, 4 of 4)

- [Federated Learning on Edge Sensing Devices: A Review](https://mlsanity.com/paper/2311.01201/) (2023)
- [Intrinsic Training Signals for Federated Learning Aggregation](https://mlsanity.com/paper/2507.06813/) (2025)
- [Federated Learning for Anomaly Detection in Maritime Movement Data](https://mlsanity.com/paper/2512.04635/) (2025)
- [Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systems](https://mlsanity.com/paper/2609.19337/) (2026)

## Abstract (arXiv)

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting.