% Key papers for Communication-efficient heterogeneous FL (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2021_federated_30088,
  title = {{Federated Learning Based on Dynamic Regularization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2748},
}

@inproceedings{neurips2017_federated_56316,
  title = {{Federated Multi-Task Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9220},
}

@inproceedings{aaai2022_federated_2105,
  title = {{Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06080-federated-dynamic-sparse-training-computing-less-communicating-less-yet-learning-better/},
}

@inproceedings{iclr2020_federated_29351,
  title = {{Federated Learning with Matched Averaging}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BkluqlSFDS},
}

@inproceedings{icml2020_fedboost_38211,
  title = {{FedBoost: A Communication-Efficient Algorithm for Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6733},
}

@inproceedings{aaai2022_spreadgnn_2774,
  title = {{SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06865-spreadgnn-decentralized-multi-task-federated-learning-for-graph-neural-networks-on-molecular-data/},
}

@inproceedings{neurips2020_throughput_60971,
  title = {{Throughput-Optimal Topology Design for Cross-Silo Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19023},
}

@inproceedings{icml2022_dispfl_40457,
  title = {{DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17221},
}

@inproceedings{icml2022_dadaquant_40396,
  title = {{DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16009},
}

@inproceedings{neurips2021_sageflow_63258,
  title = {{Sageflow: Robust Federated Learning against Both Stragglers and Adversaries}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27819},
}

@inproceedings{cvpr2024_mixed_24158,
  title = {{Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30436},
}

@inproceedings{neurips2021_delayed_61547,
  title = {{Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27907},
}

@inproceedings{aaai2022_smartidx_2755,
  title = {{SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04254-smartidx-reducing-communication-cost-in-federated-learning-by-exploiting-the-cnns-structures/},
}

@inproceedings{cvpr2024_federated_23466,
  title = {{Federated Online Adaptation for Deep Stereo}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29186},
}

@inproceedings{iclr2022_fedpara_31488,
  title = {{FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6263},
}

@inproceedings{neurips2023_fedgcn_69461,
  title = {{FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70450},
}

@inproceedings{neurips2024_heterogeneity_71448,
  title = {{Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95808},
}

@inproceedings{icml2023_fed_42161,
  title = {{Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24976},
}

@inproceedings{cvpr2023_adaptive_20349,
  title = {{Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21511},
}

@inproceedings{neurips2021_deepreduce_62089,
  title = {{DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28130},
}

@inproceedings{neurips2024_spafl_73066,
  title = {{SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94360},
}

@inproceedings{aaai2024_federated_6000,
  title = {{Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30045},
}

@inproceedings{aaai2025_dualgfl_8832,
  title = {{DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33746},
}

@inproceedings{iclr2024_fedcompass_35195,
  title = {{FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17878},
}

@inproceedings{aaai2025_whale_11261,
  title = {{WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34272},
}

@inproceedings{neurips2025_efficient_78355,
  title = {{Efficient Adaptive Federated Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=dopfjQFr65},
}

@inproceedings{aaai2026_tackling_107627,
  title = {{Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39979},
}

@inproceedings{aaai2026_ripple_107682,
  title = {{Ripple Shapley: Data Influence Attribution in One Federated Training Run}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40034},
}

@inproceedings{aaai2025_a_7894,
  title = {{A High-Efficiency Federated Learning Method Using Complementary Pruning for D2D Communication (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35318},
}

@inproceedings{iclr2024_incentivized_35666,
  title = {{Incentivized Truthful Communication for Federated Bandits}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17407},
}

@inproceedings{icml2024_pursuing_44997,
  title = {{Pursuing Overall Welfare in Federated Learning through Sequential Decision Making}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33455},
}

@inproceedings{icml2024_fedbat_45724,
  title = {{FedBAT: Communication-Efficient Federated Learning via Learnable Binarization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32728},
}

@inproceedings{icml2025_the_47823,
  title = {{The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44777},
}

@inproceedings{icml2025_secemb_48295,
  title = {{SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44319},
}

@inproceedings{icml2026_ho_50625,
  title = {{HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=EETUbVsG6X},
}

@inproceedings{neurips2025_diffusion_74793,
  title = {{Diffusion Federated Dataset}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=1GCWcrZTX8},
}

@inproceedings{neurips2025_covariances_75335,
  title = {{Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=71e1UmCMQ9},
}