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Federated Continual Learning with Weighted Inter-client Transfer

arXiv 2003.03196 ↗First posted 6 Mar 2020cs.LG stat.MLcited by 158

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Abstract (arXiv)

There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks in real-world scenarios. Yet little research has been done regarding the scenario where each client learns on a sequence of tasks from a private local data stream. This problem of federated continual learning poses new challenges to continual learning, such as utilizing knowledge from other clients, while preventing interference from irrelevant knowledge. To resolve these issues, we propose a novel federated continual learning framework, Federated Weighted Inter-client Transfer (FedWeIT), which decomposes the network weights into global federated parameters and sparse task-specific parameters, and each client receives selective knowledge from other clients by taking a weighted combination of their task-specific parameters. FedWeIT minimizes interference between incompatible tasks, and also allows positive knowledge transfer across clients during learning. We validate our FedWeIT against existing federated learning and continual learning methods under varying degrees of task similarity across clients, and our model significantly outperforms them with a large reduction in the communication cost. Code is available at https://github.com/wyjeong/FedWeIT

Key facts. Cited by 158 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 19; 2023: 34; 2024: 43; 2025: 30; 2026: 18.

158 papers.

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  1. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 860
  2. 2021Online Coreset Selection for Rehearsal-based Continual Learningcited by 114
  3. 2021Preservation of the Global Knowledge by Not-True Distillation in Federated Learningcited by 87
  4. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  5. 2023A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learningcited by 66
  6. 2023TARGET: Federated Class-Continual Learning via Exemplar-Free Distillationcited by 64
  7. 2021Recent Advances of Continual Learning in Computer Vision: An Overviewcited by 55
  8. 2024Dual-Personalizing Adapter for Federated Foundation Modelscited by 54
  9. 2021Model Zoo: A Growing "Brain" That Learns Continuallycited by 53
  10. 2023No One Left Behind: Real-World Federated Class-Incremental Learningcited by 53
  11. 2023Better Generative Replay for Continual Federated Learningcited by 53
  12. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
38 more
  1. 2021A distillation-based approach integrating continual learning and federated learning for pervasive servicescited by 42
  2. 2023A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Taskscited by 41
  3. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  4. 2022FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reductioncited by 35
  5. 2024Towards Efficient Replay in Federated Incremental Learningcited by 33
  6. 2021Concept drift detection and adaptation for federated and continual learningcited by 32
  7. 2021Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Datacited by 31
  8. 2023Asynchronous Federated Continual Learningcited by 29
  9. 2021CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparabilitycited by 28
  10. 2023Integration of Large Language Models and Federated Learningcited by 28
  11. 2022How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Compositioncited by 27
  12. 2023GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgettingcited by 27
  13. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  14. 2021Towards Federated Learning on Time-Evolving Heterogeneous Datacited by 23
  15. 2023FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformercited by 23
  16. 2024Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learningcited by 23
  17. 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Surveycited by 23
  18. 2023Exploring Continual Learning for Code Generation Modelscited by 22
  19. 2024Closed-form merging of parameter-efficient modules for Federated Continual Learningcited by 22
  20. 2023Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learningcited by 21
  21. 2024Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matchingcited by 21
  22. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  23. 2022SphereFed: Hyperspherical Federated Learningcited by 19
  24. 2024PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learningcited by 19
  25. 2021Adversarial training in communication constrained federated learningcited by 17
  26. 2022Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whiteningcited by 16
  27. 2024Text-Enhanced Data-free Approach for Federated Class-Incremental Learningcited by 16
  28. 2022Knowledge-Aware Federated Active Learning with Non-IID Datacited by 15
  29. 2023Federated Learning for Data Streamscited by 15
  30. 2024Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligencecited by 15
  31. 2021Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learningcited by 14
  32. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  33. 2022Spatial-Temporal Federated Learning for Lifelong Person Re-identification on Distributed Edgescited by 12
  34. 2023Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learningcited by 12
  35. 2024Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcarecited by 12
  36. 2021Ex-Model: Continual Learning from a Stream of Trained Modelscited by 11
  37. 2022Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantizationcited by 11
  38. 2025Accurate Forgetting for Heterogeneous Federated Continual Learningcited by 11

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