% Key papers for Client participation analysis (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2020_tackling_60913,
  title = {{Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18321},
}

@inproceedings{icml2019_agnostic_37147,
  title = {{Agnostic Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4100},
}

@inproceedings{iclr2020_fair_29366,
  title = {{Fair Resource Allocation in Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=ByexElSYDr},
}

@inproceedings{cvpr2022_fedcor_18840,
  title = {{FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2023_faster_3672,
  title = {{Faster Adaptive Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26235},
}

@inproceedings{aaai2021_toward_1524,
  title = {{Toward Understanding the Influence of Individual Clients in Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10560-toward-understanding-the-influence-of-individual-clients-in-federated-learning/},
}

@inproceedings{iclr2021_federated_30144,
  title = {{Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2670},
}

@inproceedings{icml2021_clustered_39138,
  title = {{Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10543},
}

@inproceedings{cvpr2023_on_21761,
  title = {{On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21139},
}

@inproceedings{neurips2021_linear_62886,
  title = {{Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27247},
}

@inproceedings{cvpr2024_communication_23000,
  title = {{Communication-Efficient Federated Learning with Accelerated Client Gradient}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31583},
}

@inproceedings{aaai2023_beyond_3179,
  title = {{Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26212},
}

@inproceedings{neurips2021_breaking_61740,
  title = {{Breaking the centralized barrier for cross-device federated learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27568},
}

@inproceedings{icml2022_federated_40562,
  title = {{Federated Learning with Partial Model Personalization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16615},
}

@inproceedings{aaai2022_implicit_2227,
  title = {{Implicit Gradient Alignment in Distributed and Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06454-implicit-gradient-alignment-in-distributed-and-federated-learning/},
}

@inproceedings{neurips2022_a_66084,
  title = {{A Unified Analysis of Federated Learning with Arbitrary Client Participation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53667},
}

@inproceedings{aaai2024_on_6838,
  title = {{On the Role of Server Momentum in Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29439},
}

@inproceedings{iclr2023_where_32457,
  title = {{Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11439},
}

@inproceedings{iclr2022_byzantine_31601,
  title = {{Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6400},
}

@inproceedings{neurips2023_convergence_67388,
  title = {{Convergence Analysis of Sequential Federated Learning on Heterogeneous Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72341},
}

@inproceedings{aaai2024_a_5056,
  title = {{A Primal-Dual Algorithm for Hybrid Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29363},
}

@inproceedings{neurips2024_federated_72554,
  title = {{Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94818},
}

@inproceedings{aaai2024_performative_6944,
  title = {{Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29191},
}

@inproceedings{neurips2024_efficient_71128,
  title = {{Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96094},
}

@inproceedings{icml2024_lessons_44994,
  title = {{Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33458},
}

@inproceedings{iclr2024_fedhyper_34187,
  title = {{FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18888},
}

@inproceedings{iclr2024_an_33867,
  title = {{An improved analysis of per-sample and per-update clipping in federated learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19208},
}

@inproceedings{iclr2024_a_34671,
  title = {{A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18403},
}

@inproceedings{iclr2024_bayesian_35516,
  title = {{Bayesian Coreset Optimization for Personalized Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17557},
}

@inproceedings{icml2024_byzantine_44045,
  title = {{Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34407},
}

@inproceedings{icml2024_a_45507,
  title = {{A New Theoretical Perspective on Data Heterogeneity in Federated Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32945},
}

@inproceedings{icml2024_understanding_45840,
  title = {{Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35294},
}

@inproceedings{icml2025_widening_45876,
  title = {{Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46667},
}

@inproceedings{icml2026_delayed_51253,
  title = {{Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=KG4CjK6j8Y},
}

@inproceedings{icml2026_fedqueue_51626,
  title = {{FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=ODWi7lbMGU},
}

@inproceedings{icml2026_server_52280,
  title = {{Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=UHuu4jgy2u},
}

@inproceedings{icml2026_class_54309,
  title = {{Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=njBsluG3Nn},
}

@inproceedings{neurips2024_on_72024,
  title = {{On Sampling Strategies for Spectral Model Sharding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95292},
}