% Key papers for Efficient local training in FL (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2022_splitfed_2773,
  title = {{SplitFed: When Federated Learning Meets Split Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08485-splitfed-when-federated-learning-meets-split-learning/},
}

@inproceedings{iclr2021_heterofl_30334,
  title = {{HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3165},
}

@inproceedings{cvpr2022_local_19263,
  title = {{Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2020_group_59977,
  title = {{Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18310},
}

@inproceedings{cvpr2023_feddm_20985,
  title = {{FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/20998},
}

@inproceedings{aaai2024_fedasmu_5984,
  title = {{FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29297},
}

@inproceedings{iclr2021_fedmix_30264,
  title = {{FedMix: Approximation of Mixup under Mean Augmented Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3145},
}

@inproceedings{aaai2023_layer_3977,
  title = {{Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26023},
}

@inproceedings{aaai2023_complement_3289,
  title = {{Complement Sparsification: Low-Overhead Model Pruning for Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25977},
}

@inproceedings{aaai2022_efficient_2020,
  title = {{Efficient Device Scheduling with Multi-Job Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/09971-efficient-device-scheduling-with-multi-job-federated-learning/},
}

@inproceedings{aaai2022_is_2269,
  title = {{Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/07859-is-your-data-relevant-dynamic-selection-of-relevant-data-for-federated-learning/},
}

@inproceedings{cvpr2022_federated_18844,
  title = {{Federated Learning With Position-Aware Neurons}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2021_federated_61833,
  title = {{Federated Reconstruction: Partially Local Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28043},
}

@inproceedings{cvpr2024_fedmef_23461,
  title = {{FedMef: Towards Memory-efficient Federated Dynamic Pruning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30378},
}

@inproceedings{aaai2021_federated_611,
  title = {{Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10355-federated-block-coordinate-descent-scheme-for-learning-global-and-personalized-models/},
}

@inproceedings{icml2022_progfed_41037,
  title = {{ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16193},
}

@inproceedings{iclr2018_distributed_28543,
  title = {{Distributed Fine-tuning of Language Models on Private Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/326},
}

@inproceedings{iclr2022_zerofl_30832,
  title = {{ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6036},
}

@inproceedings{neurips2023_every_67173,
  title = {{Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72537},
}

@inproceedings{aaai2024_fedlps_5993,
  title = {{FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29181},
}

@inproceedings{aaai2024_turbosvm_7660,
  title = {{TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29481},
}

@inproceedings{cvpr2024_device_23162,
  title = {{Device-Wise Federated Network Pruning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31028},
}

@inproceedings{icml2024_federated_44871,
  title = {{Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33581},
}

@inproceedings{aaai2025_decentralized_8657,
  title = {{Decentralized Federated Learning with Model Caching on Mobile Agents}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35429},
}

@inproceedings{aaai2025_gas_9264,
  title = {{GAS: Generative Activation-Aided Asynchronous Split Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35503},
}

@inproceedings{cvpr2025_afl_25391,
  title = {{AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34602},
}

@inproceedings{aaai2025_fedpop_9167,
  title = {{FedPop: Federated Population-based Hyperparameter Tuning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33732},
}

@inproceedings{aaai2025_fedspu_9169,
  title = {{FedSPU: Personalized Federated Learning for Resource-Constrained Devices with Stochastic Parameter Update}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34172},
}

@inproceedings{aaai2025_trail_10914,
  title = {{TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33524},
}

@inproceedings{neurips2024_revisiting_70702,
  title = {{Revisiting Ensembling in One-Shot Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96422},
}

@inproceedings{aaai2025_an_8104,
  title = {{An Efficient and Accurate Dynamic Sparse Training Framework Based on Parameter-Freezing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34019},
}

@inproceedings{neurips2024_why_70588,
  title = {{Why Go Full? Elevating Federated Learning Through Partial Network Updates}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96529},
}

@inproceedings{aaai2025_fedcross_9162,
  title = {{FedCross: Intertemporal Federated Learning Under Evolutionary Games}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34104},
}

@inproceedings{iclr2024_effective_35315,
  title = {{Effective and Efficient Federated Tree Learning on Hybrid Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17758},
}

@inproceedings{neurips2024_local_70147,
  title = {{Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96934},
}

@inproceedings{neurips2024_thinking_73077,
  title = {{Thinking Forward: Memory-Efficient Federated Finetuning of Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94351},
}

@inproceedings{neurips2024_low_74382,
  title = {{Low Precision Local Training is Enough for Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93183},
}