% Key papers for Differentially private federated learning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2020_federated_102234,
  title = {{Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6096},
}

@inproceedings{cvpr2022_differentially_18665,
  title = {{Differentially Private Federated Learning With Local Regularization and Sparsification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2021_flame_586,
  title = {{FLAME: Differentially Private Federated Learning in the Shuffle Model}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08688-flame-differentially-private-federated-learning-in-the-shuffle-model/},
}

@inproceedings{cvpr2023_make_21514,
  title = {{Make Landscape Flatter in Differentially Private Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22315},
}

@inproceedings{neurips2021_differentially_62216,
  title = {{Differentially Private Learning with Adaptive Clipping}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26681},
}

@inproceedings{icml2022_understanding_41299,
  title = {{Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16803},
}

@inproceedings{neurips2022_on_64822,
  title = {{On Privacy and Personalization in Cross-Silo Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54701},
}

@inproceedings{aaai2023_dpauc_3393,
  title = {{DPAUC: Differentially Private AUC Computation in Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26770},
}

@inproceedings{neurips2025_enabling_75105,
  title = {{Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers, and Gradient Clipping}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=4HZaFk9O4r},
}

@inproceedings{neurips2021_differentially_62110,
  title = {{Differentially Private Federated Bayesian Optimization with Distributed Exploration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26102},
}

@inproceedings{icml2025_clients_46486,
  title = {{Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46080},
}

@inproceedings{neurips2023_amplified_70066,
  title = {{(Amplified) Banded Matrix Factorization: A unified approach to private training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/69906},
}

@inproceedings{neurips2025_fedsvd_77127,
  title = {{FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Qq19n9LZ97},
}

@inproceedings{iclr2025_privacy_81387,
  title = {{Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30385},
}

@inproceedings{aaai2025_federated_9173,
  title = {{Federated Binary Matrix Factorization Using Proximal Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33773},
}

@inproceedings{icml2023_from_41793,
  title = {{From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24845},
}

@inproceedings{neurips2025_differentially_77417,
  title = {{Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=TecJ926Vgn},
}

@inproceedings{icml2024_noise_45719,
  title = {{Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32733},
}

@inproceedings{icml2025_leveraging_46009,
  title = {{Leveraging Randomness in Model and Data Partitioning for Privacy Amplification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46537},
}

@inproceedings{icml2025_dmm_47111,
  title = {{DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45476},
}

@inproceedings{icml2025_towards_47215,
  title = {{Towards Trustworthy Federated Learning with Untrusted Participants}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45372},
}

@inproceedings{icml2025_private_47773,
  title = {{Private Federated Learning using Preference-Optimized Synthetic Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44827},
}

@inproceedings{icml2026_continual_52621,
  title = {{Continual Learning With Participation Privacy: An Auditable Buffering-Aggregation Recipe}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=XfmTSEvETM},
}

@inproceedings{neurips2025_sketched_75653,
  title = {{Sketched Gaussian Mechanism for Private Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=AUs0rScwK0},
}

@inproceedings{neurips2025_mitigating_77857,
  title = {{Mitigating the Privacy–Utility Trade-off in Decentralized Federated Learning via f-Differential Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=YIGUv0BZCy},
}

@inproceedings{iclr2025_differentially_82405,
  title = {{Differentially Private Federated Learning with Time-Adaptive Privacy Spending}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29388},
}

@inproceedings{iclr2026_convergent_84859,
  title = {{Convergent Differential Privacy Analysis for General Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=7Zbe5ad3eX},
}

@inproceedings{cvpr2026_personalized_95838,
  title = {{Personalized Federated Training of Diffusion Models with Privacy Guarantees}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Patel_Personalized_Federated_Training_of_Diffusion_Models_with_Privacy_Guarantees_CVPR_2026_paper.html},
}

@inproceedings{cvpr2026_fully_96740,
  title = {{Fully Decentralized Certified Unlearning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Lamri_Fully_Decentralized_Certified_Unlearning_CVPR_2026_paper.html},
}

@inproceedings{cvpr2026_dp_98635,
  title = {{DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Liu_DP-FedAdamW_An_Efficient_Optimizer_for_Differentially_Private_Federated_Large_Models_CVPR_2026_paper.html},
}

@inproceedings{cvpr2026_taming_99066,
  title = {{Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Wang_Taming_Noise-Induced_Prototype_Degradation_for_Privacy-Preserving_Personalized_Federated_Fine-Tuning_CVPR_2026_paper.html},
}