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

@inproceedings{aaai2021_provably_1210,
  title = {{Provably Secure Federated Learning against Malicious Clients}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06885-provably-secure-federated-learning-against-malicious-clients/},
}

@inproceedings{aaai2022_fedinv_2102,
  title = {{FedInv: Byzantine-Robust Federated Learning by Inversing Local Model Updates}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/09171-fedinv-byzantine-robust-federated-learning-by-inversing-local-model-updates/},
}

@inproceedings{aaai2023_on_4291,
  title = {{On the Vulnerability of Backdoor Defenses for Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26393},
}

@inproceedings{icml2021_crfl_39120,
  title = {{CRFL: Certifiably Robust Federated Learning against Backdoor Attacks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9201},
}

@inproceedings{aaai2023_defl_3410,
  title = {{DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26271},
}

@inproceedings{cvpr2024_revamping_24649,
  title = {{Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30249},
}

@inproceedings{neurips2023_a3fl_68160,
  title = {{A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71628},
}

@inproceedings{icml2022_neurotoxin_40895,
  title = {{Neurotoxin: Durable Backdoors in Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18207},
}

@inproceedings{cvpr2024_byzantine_22887,
  title = {{Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29613},
}

@inproceedings{cvpr2025_model_26983,
  title = {{Model Poisoning Attacks to Federated Learning via Multi-Round Consistency}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32991},
}

@inproceedings{neurips2024_rflpa_73549,
  title = {{RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93930},
}

@inproceedings{aaai2024_a_5034,
  title = {{A Huber Loss Minimization Approach to Byzantine Robust Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30181},
}

@inproceedings{cvpr2025_fedmia_26249,
  title = {{FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33336},
}

@inproceedings{cvpr2025_detecting_25909,
  title = {{Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32667},
}

@inproceedings{aaai2024_resisting_7118,
  title = {{Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29385},
}

@inproceedings{iclr2023_flip_32734,
  title = {{FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10762},
}

@inproceedings{neurips2021_fl_61498,
  title = {{FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27252},
}

@inproceedings{neurips2024_dual_71203,
  title = {{Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96030},
}

@inproceedings{neurips2022_learning_63865,
  title = {{Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54527},
}

@inproceedings{aaai2025_label_9696,
  title = {{Label-Free Backdoor Attacks in Vertical Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34246},
}

@inproceedings{aaai2025_sadba_10587,
  title = {{SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33820},
}

@inproceedings{cvpr2025_fortifying_26304,
  title = {{Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34072},
}

@inproceedings{aaai2025_defending_8686,
  title = {{Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34733},
}

@inproceedings{aaai2024_adversarial_5144,
  title = {{Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27796},
}

@inproceedings{aaai2025_rethinking_10533,
  title = {{Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33455},
}

@inproceedings{aaai2024_emgan_5781,
  title = {{EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29258},
}

@inproceedings{aaai2025_attribute_8173,
  title = {{Attribute Inference Attacks for Federated Regression Tasks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33787},
}

@inproceedings{neurips2024_parameter_73278,
  title = {{Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94163},
}

@inproceedings{neurips2024_federated_70506,
  title = {{Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96602},
}

@inproceedings{neurips2025_tracing_78384,
  title = {{Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=e5Evni5zZY},
}