mlsanityData to 7 Oct 2026

Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning

arXiv 2202.03070 ↗First posted 7 Feb 2022cs.LGcited by 6

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Abstract (arXiv)

Online federated learning (OFL) and online transfer learning (OTL) are two collaborative paradigms for overcoming modern machine learning challenges such as data silos, streaming data, and data security. This survey explored OFL and OTL throughout their major evolutionary routes to enhance understanding of online federated and transfer learning. Besides, practical aspects of popular datasets and cutting-edge applications for online federated and transfer learning are highlighted in this work. Furthermore, this survey provides insight into potential future research areas and aims to serve as a resource for professionals developing online federated and transfer learning frameworks.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 48 of its 193 references that are in the map. First posted on arXiv 7 Feb 2022.

Builds on

48 of 193 references.

References · 48

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2016Layer Normalizationcited by 5,579
  5. 2019Advances and Open Problems in Federated Learningcited by 3,021
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2014How transferable are features in deep neural networks?cited by 1,216
  9. 2019Towards Federated Learning at Scale: System Designcited by 826
  10. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  11. 2017Federated Multi-Task Learningcited by 669
  12. 2020Personalized Federated Learning with Moreau Envelopescited by 612
36 more
  1. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  2. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  3. 2021Towards Personalized Federated Learningcited by 462
  4. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  5. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  6. 2019Federated Machine Learning: Concept and Applicationscited by 409
  7. 2019Asynchronous Federated Optimizationcited by 264
  8. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  9. 2018Online Learning: A Comprehensive Surveycited by 203
  10. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  11. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  12. 2019Federated Evaluation of On-device Personalizationcited by 147
  13. 2020Survey of Personalization Techniques for Federated Learningcited by 144
  14. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  15. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  16. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  17. 2019Peer-to-peer Federated Learning on Graphscited by 85
  18. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  19. 2016Beyond Sharing Weights for Deep Domain Adaptationcited by 80
  20. 2019Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labelingcited by 53
  21. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
  22. 2018Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learningcited by 46
  23. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  24. 2015Staleness-aware Async-SGD for Distributed Deep Learningcited by 43
  25. 2019Federated Transfer Reinforcement Learning for Autonomous Drivingcited by 43
  26. 2016Efficient Second Order Online Learning by Sketchingcited by 33
  27. 2020FLeet: Online Federated Learning via Staleness Awareness and Performance Predictioncited by 21
  28. 2019Secure and Efficient Federated Transfer Learningcited by 20
  29. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  30. 2020Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theorycited by 16
  31. 2021Communication-Efficient Online Federated Learning Framework for Nonlinear Regressioncited by 16
  32. 2019HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalographycited by 15
  33. 2021FedNILM: Applying Federated Learning to NILM Applications at the Edgecited by 11
  34. 2020Federated Transfer Learning with Dynamic Gradient Aggregationcited by 8
  35. 2021Real-time intelligent big data processing: technology, platform, and applicationscited by 3
  36. 2019Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimizationcited by 1

Built on by

Cited by year

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

6 papers.

  1. 2023Federated Learning for Data Streamscited by 15
  2. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  3. 2023A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regretcited by 7
  4. 2023Federated Online and Bandit Convex Optimizationcited by 7
  5. 2022A Survey on Distributed Online Optimization and Gamecited by 1
  6. 2025Enhancing Privacy Preservation and Reducing Analysis Time with Federated Transfer Learning in Digital Twins-based Computed Tomography Scan Analysiscited by 0