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

@inproceedings{icml2020_scaffold_38718,
  title = {{SCAFFOLD: Stochastic Controlled Averaging for Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5880},
}

@inproceedings{icml2025_compressed_46048,
  title = {{Compressed and distributed least-squares regression: convergence rates with applications to federated learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46714},
}

@inproceedings{neurips2020_lower_60274,
  title = {{Lower Bounds and Optimal Algorithms for Personalized Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17633},
}

@inproceedings{icml2020_fetchsgd_38213,
  title = {{FetchSGD: Communication-Efficient Federated Learning with Sketching}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6724},
}

@inproceedings{aaai2021_on_1095,
  title = {{On the Convergence of Communication-Efficient Local SGD for Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07510-on-the-convergence-of-communication-efficient-local-sgd-for-federated-learning/},
}

@inproceedings{icml2020_from_38232,
  title = {{From Local SGD to Local Fixed-Point Methods for Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6590},
}

@inproceedings{icml2021_federated_39342,
  title = {{Federated Composite Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10485},
}

@inproceedings{iclr2023_single_31983,
  title = {{Single-shot General Hyper-parameter Optimization for Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/12042},
}

@inproceedings{icml2021_bias_39101,
  title = {{Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9507},
}

@inproceedings{icml2022_fednl_40557,
  title = {{FedNL: Making Newton-Type Methods Applicable to Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17083},
}

@inproceedings{neurips2021_feddr_62277,
  title = {{FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27479},
}

@inproceedings{neurips2022_soteriafl_66247,
  title = {{SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53166},
}

@inproceedings{icml2022_personalization_41003,
  title = {{Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16591},
}

@inproceedings{icml2023_fedhpo_41685,
  title = {{FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24231},
}

@inproceedings{neurips2022_tct_65783,
  title = {{TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54807},
}

@inproceedings{neurips2023_zeroth_66760,
  title = {{Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72874},
}

@inproceedings{aaai2024_fedns_5995,
  title = {{FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29254},
}

@inproceedings{icml2022_compressed_40346,
  title = {{Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16395},
}

@inproceedings{icml2022_fednew_40559,
  title = {{FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16853},
}

@inproceedings{icml2023_the_42437,
  title = {{The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24679},
}

@inproceedings{neurips2025_robust_78389,
  title = {{Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=e8DrPuJekZ},
}

@inproceedings{aaai2024_federated_6007,
  title = {{Federated X-armed Bandit}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29267},
}

@inproceedings{neurips2024_nonconvex_74283,
  title = {{Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93275},
}

@inproceedings{neurips2024_communication_74483,
  title = {{Communication-Efficient Federated Group Distributionally Robust Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93092},
}

@inproceedings{aaai2025_efskip_8865,
  title = {{EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33700},
}

@inproceedings{neurips2024_scafflsa_71440,
  title = {{SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95816},
}

@inproceedings{iclr2024_stochastic_35061,
  title = {{Stochastic Controlled Averaging for Federated Learning with Communication Compression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18012},
}

@inproceedings{neurips2024_the_70599,
  title = {{The Sample-Communication Complexity Trade-off in Federated Q-Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96519},
}

@inproceedings{aaai2026_fedadamw_107197,
  title = {{FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39549},
}

@inproceedings{iclr2024_federated_34894,
  title = {{Federated Q-Learning: Linear Regret Speedup with Low Communication Cost}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18180},
}

@inproceedings{iclr2024_communication_35207,
  title = {{Communication-Efficient Federated Non-Linear Bandit Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17866},
}

@inproceedings{icml2024_decomposable_44433,
  title = {{Decomposable Submodular Maximization in Federated Setting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34019},
}

@inproceedings{icml2024_private_45544,
  title = {{Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32908},
}

@inproceedings{icml2024_achieving_45671,
  title = {{Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32781},
}

@inproceedings{icml2025_scaffold_45966,
  title = {{Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46580},
}

@inproceedings{icml2025_privacy_47470,
  title = {{Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45123},
}

@inproceedings{neurips2024_does_73498,
  title = {{Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93978},
}

@inproceedings{neurips2025_sharp_78115,
  title = {{Sharp Gaussian approximations for Decentralized Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=b7waOsMnq8},
}