mlsanityData to 7 Oct 2026

Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning

arXiv 2307.04869 ↗First posted 10 Jul 2023cs.LG cs.AI cs.CVcited by 12

Not on the map. Why?

Abstract (arXiv)

Federated continual learning (FCL) learns incremental tasks over time from confidential datasets distributed across clients. This paper focuses on rehearsal-free FCL, which has severe forgetting issues when learning new tasks due to the lack of access to historical task data. To address this issue, we propose Fed-CPrompt based on prompt learning techniques to obtain task-specific prompts in a communication-efficient way. Fed-CPrompt introduces two key components, asynchronous prompt learning, and contrastive continual loss, to handle asynchronous task arrival and heterogeneous data distributions in FCL, respectively. Extensive experiments demonstrate the effectiveness of Fed-CPrompt in achieving SOTA rehearsal-free FCL performance.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 10 Jul 2023.

Builds on

26 of 36 references.

References · 26

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  5. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  6. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  7. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  8. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  9. 2016Progressive Neural Networkscited by 1,332
  10. 2019How Can We Know What Language Models Know?cited by 953
  11. 2021Learning to Prompt for Continual Learningcited by 938
  12. 2021Model-Contrastive Federated Learningcited by 762
14 more
  1. 2022DualPrompt: Complementary Prompting for Rehearsal-free Continual Learningcited by 635
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  3. 2022CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learningcited by 446
  4. 2019Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgettingcited by 302
  5. 2020AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Promptscited by 246
  6. 2020Federated Continual Learning with Weighted Inter-client Transfercited by 158
  7. 2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 133
  8. 2022Federated Class-Incremental Learningcited by 126
  9. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  10. 2022A Closer Look at Rehearsal-Free Continual Learningcited by 54
  11. 2021A distillation-based approach integrating continual learning and federated learning for pervasive servicescited by 42
  12. 2021Concept drift detection and adaptation for federated and continual learningcited by 32
  13. 2023Asynchronous Federated Continual Learningcited by 29
  14. 2022Federated Continual Learning through distillation in pervasive computingcited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 5; 2026: 1.

12 papers.

  1. 2024Closed-form merging of parameter-efficient modules for Federated Continual Learningcited by 22
  2. 2024PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learningcited by 19
  3. 2024FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfercited by 9
  4. 2024Federated Class-Incremental Learning with Hierarchical Generative Prototypescited by 6
  5. 2024Masked Autoencoders are Parameter-Efficient Federated Continual Learnerscited by 3
  6. 2024FPPL: An Efficient and Non-IID Robust Federated Continual Learning Frameworkcited by 2
  7. 2025C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learningcited by 2
  8. 2025Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuningcited by 1
  9. 2025Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregationcited by 1
  10. 2025FedGTEA: Federated Class-Incremental Learning with Gaussian Task Embedding and Alignmentcited by 1
  11. 2025DOLFIN: Balancing Stability and Plasticity in Federated Continual Learningcited by 0
  12. 2026SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learningcited by 0