% Key papers for Federated prompt and LoRA tuning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2021_federated_39343,
  title = {{Federated Continual Learning with Weighted Inter-client Transfer}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9849},
}

@inproceedings{neurips2024_federated_73321,
  title = {{Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94124},
}

@inproceedings{cvpr2024_global_23632,
  title = {{Global and Local Prompts Cooperation via Optimal Transport for Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30483},
}

@inproceedings{neurips2024_flora_72322,
  title = {{FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95025},
}

@inproceedings{aaai2022_cross_1903,
  title = {{Cross-Modal Federated Human Activity Recognition via Modality-Agnostic and Modality-Specific Representation Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/03063-cross-modal-federated-human-activity-recognition-via-modality-agnostic-and-modality-specific-representation-learning/},
}

@inproceedings{aaai2021_decentralised_366,
  title = {{Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/02898-decentralised-learning-from-independent-multi-domain-labels-for-person-re-identification/},
}

@inproceedings{cvpr2020_unsupervised_16424,
  title = {{Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{aaai2024_federated_5998,
  title = {{Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29434},
}

@inproceedings{cvpr2024_traceable_25070,
  title = {{Traceable Federated Continual Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29684},
}

@inproceedings{cvpr2022_local_19265,
  title = {{Local-Adaptive Face Recognition via Graph-Based Meta-Clustering and Regularized Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{iclr2023_on_32940,
  title = {{On the Importance and Applicability of Pre-Training for Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10799},
}

@inproceedings{aaai2025_fedpia_9166,
  title = {{FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34228},
}

@inproceedings{iclr2024_improving_34283,
  title = {{Improving LoRA in Privacy-preserving Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18792},
}

@inproceedings{cvpr2025_f_26228,
  title = {{F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34425},
}

@inproceedings{aaai2026_fedalt_106747,
  title = {{FedALT: Federated Fine-Tuning Through Adaptive Local Training with Rest-of-World LoRA}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39054},
}

@inproceedings{cvpr2025_population_27277,
  title = {{Population Normalization for Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/35202},
}

@inproceedings{neurips2024_federated_72661,
  title = {{Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94723},
}

@inproceedings{neurips2025_learning_74998,
  title = {{Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=3FBByWp6GL},
}

@inproceedings{aaai2026_transfr_107696,
  title = {{TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40048},
}

@inproceedings{aaai2025_rethinking_10542,
  title = {{Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33764},
}

@inproceedings{icml2024_harmonizing_44683,
  title = {{Harmonizing Generalization and Personalization in Federated Prompt Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33769},
}

@inproceedings{icml2026_federated_54568,
  title = {{Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=qFKFSVvff5},
}

@inproceedings{neurips2024_clues_71940,
  title = {{CLUES: Collaborative Private-domain High-quality Data Selection for LLMs via Training Dynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95368},
}

@inproceedings{neurips2024_cooperative_74672,
  title = {{Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92924},
}

@inproceedings{neurips2025_towards_75349,
  title = {{Towards Robust Parameter-Efficient Fine-Tuning for Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=790HoTFKUE},
}

@inproceedings{neurips2025_you_76129,
  title = {{You Only Communicate Once: One-shot Federated Low-Rank Adaptation of MLLM}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=FoVF3iL6o3},
}