mlsanityData to 13 Oct 2026

Failure Prediction in Production Line Based on Federated Learning: An Empirical Study

arXiv 2101.11715 ↗First posted 25 Jan 2021cs.LGcited by 11

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Abstract (arXiv)

Data protection across organizations is limiting the application of centralized learning (CL) techniques. Federated learning (FL) enables multiple participants to build a learning model without sharing data. Nevertheless, there are very few research works on FL in intelligent manufacturing. This paper presents the results of an empirical study on failure prediction in the production line based on FL. This paper (1) designs Federated Support Vector Machine (FedSVM) and Federated Random Forest (FedRF) algorithms for the horizontal FL and vertical FL scenarios, respectively; (2) proposes an experiment process for evaluating the effectiveness between the FL and CL algorithms; (3) finds that the performance of FL and CL are not significantly different on the global testing data, on the random partial testing data, and on the estimated unknown Bosch data, respectively. The fact that the testing data is heterogeneous enhances our findings. Our study reveals that FL can replace CL for failure prediction.

Key facts. Cited by 11 papers in the map (data through 13 Oct 2026). Builds on 28 of its 69 references that are in the map. First posted on arXiv 25 Jan 2021.

Builds on

28 of 69 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2019Advances and Open Problems in Federated Learningcited by 3,040
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  5. 2019Federated Machine Learning: Concept and Applicationscited by 411
  6. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  7. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 149
  8. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 117
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  10. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  11. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 71
  12. 2019Variational Federated Multi-Task Learningcited by 69
16 more
  1. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  2. 2019Federated Forestcited by 49
  3. 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
  4. 2019Differential Privacy-enabled Federated Learning for Sensitive Health Datacited by 44
  5. 2019Federated Transfer Reinforcement Learning for Autonomous Drivingcited by 44
  6. 2019Towards Federated Graph Learning for Collaborative Financial Crimes Detectioncited by 33
  7. 2018Federated Learning for Ultra-Reliable Low-Latency V2V Communicationscited by 30
  8. 2019Federated Uncertainty-Aware Learning for Distributed Hospital EHR Datacited by 17
  9. 2017A Berkeley View of Systems Challenges for AIcited by 15
  10. 2020A Systematic Literature Review on Federated Learning: From A Model Quality Perspectivecited by 15
  11. 2019Federated and Differentially Private Learning for Electronic Health Recordscited by 13
  12. 2018FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Recordcited by 11
  13. 2020Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronauticscited by 6
  14. 2019Privacy Preserving QoE Modeling using Collaborative Learningcited by 4
  15. 2019A Federated Learning Approach for Mobile Packet Classificationcited by 4
  16. 2020Privacy-Preserving News Recommendation Model Learningcited by 3

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 3; 2025: 0; 2026: 1.

11 papers.

  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  2. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  3. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  4. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  5. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  6. 2022Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognitioncited by 6
  7. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  8. 2023Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing Systemcited by 4
  9. 2023Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applicationscited by 4
  10. 2023Federated Battery Diagnosis and Prognosiscited by 0
  11. 2026Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulationcited by 0