mlsanityData to 13 Oct 2026

When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

arXiv 2306.15546 ↗First posted 27 Jun 2023cs.LG cs.AI cs.DCcited by 84

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Abstract (arXiv)

The intersection of Foundation Model (FM) and Federated Learning (FL) presents a unique opportunity to unlock new possibilities for real-world applications. On the one hand, FL, as a collaborative learning paradigm, help address challenges in FM development by expanding data availability, enabling computation sharing, facilitating the collaborative development of FMs, tackling continuous data update, avoiding FM monopoly, response delay and FM service down. On the other hand, FM, equipped with pre-trained knowledge and exceptional performance, can serve as a robust starting point for FL. It can also generate synthetic data to enrich data diversity and enhance overall performance of FL. Meanwhile, FM unlocks new sharing paradigm and multi-task and multi-modality capabilities for FL. By examining the interplay between FL and FM, this paper presents the motivations, challenges, and future directions of empowering FL with FM and empowering FM with FL. We hope that this work provides a good foundation to inspire future research efforts to drive advancements in both fields.

Key facts. Cited by 84 papers in the map (data through 13 Oct 2026). Builds on 112 of its 183 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

112 of 183 references.

References · 112

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50 most cited of 112 All 112 (JSON)

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 11; 2024: 36; 2025: 30; 2026: 7.

84 papers.

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  1. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  2. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 114
  3. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  4. 2023FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learningcited by 59
  5. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  6. 2024Dual-Personalizing Adapter for Federated Foundation Modelscited by 55
  7. 2024Advances and Open Challenges in Federated Foundation Modelscited by 34
  8. 2024Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6Gcited by 31
  9. 2023Integration of Large Language Models and Federated Learningcited by 30
  10. 2024Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boostingcited by 27
  11. 2023FwdLLM: Efficient FedLLM using Forward Gradientcited by 25
  12. 2024Federated Large Language Models: Current Progress and Future Directionscited by 25
38 more
  1. 2023Grounding Foundation Models through Federated Transfer Learning: A General Frameworkcited by 24
  2. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  3. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  4. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  5. 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learningcited by 17
  6. 2025A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learningcited by 17
  7. 2024FedPFT: Federated Proxy Fine-Tuning of Foundation Modelscited by 16
  8. 2024Federated Learning in Practice: Reflections and Projectionscited by 15
  9. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  10. 2024Parametric Feature Transfer: One-shot Federated Learning with Foundation Modelscited by 13
  11. 2024Federated Foundation Models on Heterogeneous Time Seriescited by 13
  12. 2024Towards Multi-modal Transformers in Federated Learningcited by 12
  13. 2024Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcarecited by 12
  14. 2025Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directionscited by 10
  15. 2025FeDaL: Federated Dataset Learning for General Time Series Foundation Modelscited by 10
  16. 2023Bridging the Gap Between Foundation Models and Heterogeneous Federated Learningcited by 9
  17. 2023ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learningcited by 9
  18. 2025Vision Foundation Models in Medical Image Analysis: Advances and Challengescited by 9
  19. 2025Federated Low-Rank Adaptation for Foundation Models: A Surveycited by 9
  20. 2024FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injectioncited by 8
  21. 2025Federated Fine-Tuning of LLMs: Framework Comparison and Research Directionscited by 8
  22. 2023FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clientscited by 7
  23. 2024Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clientscited by 7
  24. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  25. 2025Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocationcited by 7
  26. 2025BetaWeb: Towards a Blockchain-enabled Trustworthy Agentic Webcited by 7
  27. 2024FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learningcited by 6
  28. 2023FDAPT: Federated Domain-adaptive Pre-training for Language Modelscited by 5
  29. 2024Research and application of artificial intelligence based webshell detection model: A literature reviewcited by 4
  30. 2024Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classificationcited by 4
  31. 2024F3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristicscited by 4
  32. 2024Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilitiescited by 3
  33. 2024Securing Federated Learning against Backdoor Threats with Foundation Model Integrationcited by 3
  34. 2024FedBaF: Federated Learning Aggregation Biased by a Foundation Modelcited by 3
  35. 2025FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregationcited by 3
  36. 2026Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Surveycited by 3
  37. 2023Federated Fine-Tuning of Foundation Models via Probabilistic Maskingcited by 2
  38. 2024Exploring Federated Self-Supervised Learning for General Purpose Audio Understandingcited by 2

50 most cited of 84 All 84 (JSON)