mlsanityData to 7 Oct 2026

Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

arXiv 2304.11101 ↗First posted 21 Apr 2023cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 21 Apr 2023.

Builds on

14 of 29 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2020Adaptive Federated Optimizationcited by 1,036
  5. 2019Federated Machine Learning: Concept and Applicationscited by 409
  6. 2019Fair Resource Allocation in Federated Learningcited by 389
  7. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  8. 2020The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systemscited by 31
  9. 2021Failure Prediction in Production Line Based on Federated Learning: An Empirical Studycited by 11
  10. 2021New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learningcited by 11
  11. 2021Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning Systemcited by 8
  12. 2021Minimal-Configuration Anomaly Detection for IIoT Sensorscited by 3
2 more
  1. 2021Interpretable Predictive Maintenance for Hard Drivescited by 2
  2. 2022Smart Active Sampling to enhance Quality Assurance Efficiencycited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2023Federated Learning on Edge Sensing Devices: A Reviewcited by 1
  2. 2025Intrinsic Training Signals for Federated Learning Aggregationcited by 0
  3. 2025Federated Learning for Anomaly Detection in Maritime Movement Datacited by 0
  4. 2026Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systemscited by 0