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

@inproceedings{aaai2020_practical_102033,
  title = {{Practical Federated Gradient Boosting Decision Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5895},
}

@inproceedings{aaai2021_secure_1347,
  title = {{Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10896-secure-bilevel-asynchronous-vertical-federated-learning-with-backward-updating/},
}

@inproceedings{aaai2022_seizing_2704,
  title = {{Seizing Critical Learning Periods in Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08788-seizing-critical-learning-periods-in-federated-learning/},
}

@inproceedings{icml2022_fedscale_40560,
  title = {{FedScale: Benchmarking Model and System Performance of Federated Learning at Scale}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16277},
}

@inproceedings{neurips2022_fairvfl_63998,
  title = {{FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54790},
}

@inproceedings{iclr2024_fair_35418,
  title = {{Fair and Efficient Contribution Valuation for Vertical Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17655},
}

@inproceedings{neurips2024_fedllm_73107,
  title = {{FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97593},
}

@inproceedings{aaai2024_complementary_5439,
  title = {{Complementary Knowledge Distillation for Robust and Privacy-Preserving Model Serving in Vertical Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29958},
}

@inproceedings{icml2024_fedbpt_43699,
  title = {{FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34753},
}

@inproceedings{icml2023_fedvs_41672,
  title = {{FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24392},
}

@inproceedings{neurips2022_coresets_64734,
  title = {{Coresets for Vertical Federated Learning: Regularized Linear Regression and \$K\$-Means Clustering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54664},
}

@inproceedings{neurips2022_a_65618,
  title = {{A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55343},
}

@inproceedings{aaai2022_federated_2107,
  title = {{Federated Nearest Neighbor Classification with a Colony of Fruit-Flies}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08036-federated-nearest-neighbor-classification-with-a-colony-of-fruit-flies/},
}

@inproceedings{icml2023_vertical_42157,
  title = {{Vertical Federated Graph Neural Network for Recommender System}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24717},
}

@inproceedings{iclr2023_federated_32581,
  title = {{Federated Nearest Neighbor Machine Translation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11368},
}

@inproceedings{iclr2024_vflair_35443,
  title = {{VFLAIR: A Research Library and Benchmark for Vertical Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17630},
}

@inproceedings{icml2023_less_42079,
  title = {{LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24492},
}

@inproceedings{neurips2022_vf_66307,
  title = {{VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53772},
}

@inproceedings{neurips2023_towards_67410,
  title = {{Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/73648},
}

@inproceedings{neurips2024_federated_71302,
  title = {{Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95945},
}

@inproceedings{aaai2024_integer_6331,
  title = {{Integer Is Enough: When Vertical Federated Learning Meets Rounding}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29388},
}

@inproceedings{iclr2024_vertibench_34942,
  title = {{VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18132},
}

@inproceedings{aaai2025_hacore_9402,
  title = {{HaCore: Efficient Coreset Construction with Locality Sensitive Hashing for Vertical Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34409},
}

@inproceedings{neurips2025_mars_75128,
  title = {{MARS-VFL: A Unified Benchmark for Vertical Federated Learning with Realistic Evaluation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=4Ud0pRqFto},
}

@inproceedings{icml2025_federated_47028,
  title = {{Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45557},
}

@inproceedings{neurips2025_vertical_79201,
  title = {{Vertical Federated Feature Screening}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=msaLqsSTyF},
}

@inproceedings{neurips2025_accelerated_79522,
  title = {{Accelerated Vertical Federated Adversarial Learning through Decoupling Layer-Wise Dependencies}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=qH70UC6SNy},
}

@inproceedings{aaai2026_equilibrium_107854,
  title = {{Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40206},
}

@inproceedings{icml2024_byzantine_45442,
  title = {{Byzantine Resilient and Fast Federated Few-Shot Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33009},
}

@inproceedings{icml2025_fedone_47279,
  title = {{FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45310},
}

@inproceedings{icml2025_improved_48719,
  title = {{Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43903},
}

@inproceedings{icml2026_influence_50682,
  title = {{Influence-Disentangled Federated Training: Learning Models That Are Easy to Unlearn}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=EoZLAz9Edj},
}

@inproceedings{neurips2025_pubsub_75697,
  title = {{PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=B5mEYUJi85},
}