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

@inproceedings{cvpr2021_model_17420,
  title = {{Model-Contrastive Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{aaai2021_personalized_1163,
  title = {{Personalized Cross-Silo Federated Learning on Non-IID Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07865-personalized-cross-silo-federated-learning-on-non-iid-data/},
}

@inproceedings{cvpr2022_fine_18860,
  title = {{Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2022_feddc_18842,
  title = {{FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2022_robust_19753,
  title = {{Robust Federated Learning With Noisy and Heterogeneous Clients}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2022_rethinking_19717,
  title = {{Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2023_rethinking_22045,
  title = {{Rethinking Federated Learning With Domain Shift: A Prototype View}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21769},
}

@inproceedings{icml2019_bayesian_37182,
  title = {{Bayesian Nonparametric Federated Learning of Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3892},
}

@inproceedings{aaai2024_fedtgp_5997,
  title = {{FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29617},
}

@inproceedings{aaai2023_efficient_3543,
  title = {{Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26197},
}

@inproceedings{cvpr2023_dafkd_20710,
  title = {{DaFKD: Domain-Aware Federated Knowledge Distillation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21464},
}

@inproceedings{cvpr2024_an_22758,
  title = {{An Aggregation-Free Federated Learning for Tackling Data Heterogeneity}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30905},
}

@inproceedings{icml2016_learning_35890,
  title = {{Learning privately from multiparty data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/hamm16.html},
}

@inproceedings{aaai2023_federated_3686,
  title = {{Federated Generative Model on Multi-Source Heterogeneous Data in IoT}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26252},
}

@inproceedings{neurips2022_fedrolex_64825,
  title = {{FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54671},
}

@inproceedings{cvpr2024_relaxed_24612,
  title = {{Relaxed Contrastive Learning for Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29785},
}

@inproceedings{neurips2022_preservation_66106,
  title = {{Preservation of the Global Knowledge by Not-True Distillation in Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53782},
}

@inproceedings{neurips2021_no_61549,
  title = {{No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27280},
}

@inproceedings{cvpr2024_an_22765,
  title = {{An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30103},
}

@inproceedings{iclr2021_fedbe_30486,
  title = {{FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2944},
}

@inproceedings{cvpr2024_feduv_23464,
  title = {{FedUV: Uniformity and Variance for Heterogeneous Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30003},
}

@inproceedings{neurips2024_classifier_70610,
  title = {{Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96509},
}

@inproceedings{aaai2024_towards_7596,
  title = {{Towards Fair Graph Federated Learning via Incentive Mechanisms}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29365},
}

@inproceedings{aaai2024_federated_5999,
  title = {{Federated Causality Learning with Explainable Adaptive Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29566},
}

@inproceedings{aaai2025_fedsa_9168,
  title = {{FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34464},
}

@inproceedings{aaai2025_cross_8565,
  title = {{Cross-Silo Feature Space Alignment for Federated Learning on Clients with Imbalanced Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34201},
}

@inproceedings{neurips2024_fedgmkd_72965,
  title = {{FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94448},
}

@inproceedings{aaai2024_on_6823,
  title = {{On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29010},
}

@inproceedings{neurips2024_confusion_71322,
  title = {{Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95925},
}

@inproceedings{cvpr2025_a_25368,
  title = {{A Simple Data Augmentation for Feature Distribution Skewed Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32937},
}

@inproceedings{aaai2024_data_5589,
  title = {{Data Disparity and Temporal Unavailability Aware Asynchronous Federated Learning for Predictive Maintenance on Transportation Fleets}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29467},
}

@inproceedings{neurips2024_fiarse_72921,
  title = {{FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94490},
}

@inproceedings{aaai2025_dchm_8592,
  title = {{DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33877},
}

@inproceedings{cvpr2024_adaptive_22726,
  title = {{Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29660},
}

@inproceedings{neurips2024_federated_70558,
  title = {{Federated Model Heterogeneous Matryoshka Representation Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96556},
}

@inproceedings{aaai2025_fed_9159,
  title = {{Fed-DFA: Federated Distillation for Heterogeneous Model Fusion Through the Adversarial Lens}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35444},
}