mlsanityData to 7 Oct 2026

A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things

arXiv 2104.10501 ↗First posted 21 Apr 2021cs.DC cs.AI cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4.0 on the edge computing level. FL solves the dilemma in which enterprises wish to make the use of data intelligence with security concerns. To accelerate industrial Internet of things with the further leverage of FL, existing achievements on FL are developed from three aspects: 1) define terminologies and elaborate a general framework of FL for accommodating various scenarios; 2) discuss the state-of-the-art of FL on fundamental researches including data partitioning, privacy preservation, model optimization, local model transportation, personalization, motivation mechanism, platform & tools, and benchmark; 3) discuss the impacts of FL from the economic perspective. To attract more attention from industrial academia and practice, a FL-transformed manufacturing paradigm is presented, and future research directions of FL are given and possible immediate applications in Industry 4.0 domain are also proposed.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 53 of its 102 references that are in the map. First posted on arXiv 21 Apr 2021.

Builds on

53 of 102 references.

References · 53

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  7. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  9. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  10. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  11. 2020Adaptive Personalized Federated Learningcited by 353
  12. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
38 more
  1. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  2. 2019Asynchronous Federated Optimizationcited by 264
  3. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  4. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  5. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  6. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  7. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  8. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  9. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
  10. 2018Blockchained On-Device Federated Learningcited by 105
  11. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  12. 2018Federated Learning for Keyword Spottingcited by 95
  13. 2018D"IoT: A Federated Self-learning Anomaly Detection System for IoTcited by 89
  14. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
  15. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  16. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  17. 2019Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systemscited by 77
  18. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
  19. 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 63
  20. 2020Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Frameworkcited by 59
  21. 2020Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcited by 56
  22. 2019Federated Forestcited by 49
  23. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  24. 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
  25. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  26. 2019Federated Transfer Reinforcement Learning for Autonomous Drivingcited by 43
  27. 2020From Federated Learning to Federated Neural Architecture Search: A Surveycited by 42
  28. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  29. 2020Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligencecited by 39
  30. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  31. 2020Adaptive Federated Learning and Digital Twin for Industrial Internet of Thingscited by 27
  32. 2019Federated Learning for Ranking Browser History Suggestionscited by 23
  33. 2019Federated and Differentially Private Learning for Electronic Health Recordscited by 13
  34. 2020From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Securitycited by 11
  35. 2021Failure Prediction in Production Line Based on Federated Learning: An Empirical Studycited by 11
  36. 2019Asynchronous Online Federated Learning for Edge Devices with Non-IID Datacited by 9
  37. 2019Quantifying the Performance of Federated Transfer Learningcited by 9
  38. 2020Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Methodcited by 9

50 most cited of 53 All 53 (JSON)

Built on by

Cited by year

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

11 papers.

  1. 2023LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learningcited by 14
  2. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  3. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  4. 2022Federated Distillation based Indoor Localization for IoT Networkscited by 6
  5. 2021Federated Natural Language Generation for Personalized Dialogue Systemcited by 5
  6. 2022FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learningcited by 3
  7. 2024FedCL-Ensemble Learning: A Framework of Federated Continual Learning with Ensemble Transfer Learning Enhanced for Alzheimer's MRI Classifications while Preserving Privacycited by 1
  8. 2025Adaptive Deadline and Batch Layered Synchronized Federated Learningcited by 1
  9. 2023A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworkscited by 0
  10. 2023Serverless Federated Learning with flwr-serverlesscited by 0
  11. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0