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English | 中文

联邦学习攻击与防御必读论文

介绍

本仓库提供了关于联邦学习（Federated Learning，FL）的精选论文和教程列表，包括系统教程、现有综述、横向联邦学习、纵向联邦学习、联邦迁移学习、联邦推荐场景、数据投毒攻击、模型投毒攻击、数据投毒防御、模型投毒防御、隐私保护的联邦学习、隐私保护的联邦推荐、联邦推荐中的现有攻击和防御方法和一些开源代码等。

* 请通过提交 pull request 并使用以下模版来帮助完善此列表，感谢所有的贡献者！

- 作者名字. **论文名称.**  会议名称/期刊名称, 发表年份.

目录

系统教程

  • Yang et al. Federated Learning. SLAIML, 2021.

现有综述

  • Zhou et al. 联邦学习研究综述. 网络与信息安全学报, 2021.
  • Liang et al. 基于联邦学习的推荐系统综述. 中国科学, 2022.
  • Wu et al. 联邦学习攻击与防御综述. 大数据, 2022.
  • Chen et al. 联邦学习攻防研究综述. 计算机科学, 2022.
  • Gu et al. 联邦学习模型安全与隐私研究进展. 软件学报, 2022.
  • Shi et al. challenges and approaches for mitigating byzantine attacks in federated learning. IEEE ICTSP, 2022.
  • Xiao et al. 联邦学习的隐私保护与安全防御研究综述. 计算机学报, 2023.
  • Gao et al. 联邦学习系统攻击与防御技术研究综述. 计算机学报, 2023.
  • Sun et al. 联邦学习拜占庭攻击与防御研究综述. 网络空间安全科学学报, 2023.
  • Yang et al. 联邦学习与攻防对抗综述. 信息网络安全, 2023.
  • Sun et
    al.Data_and_Model_Poisoning_Backdoor_Attacks_on_Wireless_Federated_Learning_and_the_Defense_Mechanisms_A_Comprehensive_Survey. IEEE CSAT, 2024.
  • Chen et al. 联邦学习中的安全威胁与防御措施综述. 计算机应用, 2024.
  • Zhang et al. 联邦学习中的攻击手段与防御机制研究综述. 计算机工程与应用, 2024.

横向联邦学习

  • Zhang et al. Federated Feature Selection for Horizontal Federated Learning in IoT Networks. IEEE IoT-J, 2023.

纵向联邦学习

  • Chen et al. 面向纵向联邦学习的对抗样本生成算法. 通信学报, 2023.
  • Lai et al. VFedAD A Defense Method Based on the Information. CIKD CCFB, 2023.

联邦迁移学习

  • Liu et al. A Secure Federated Transfer Learning Framework. IEEE IS, 2023.

联邦推荐场景

  • Chen et al. 一种基于注意力联邦蒸馏的推荐方法. 软件学报, 2020.
  • Rong et al. poisoning deep learning based recommender model in federated learning scenarios. IJCAL, 2022.

数据投毒攻击

  • Xiao et al. Adversarial label flips attack on support vector machines. ECAL, 2012.
  • Rosenfeld et al. certified robustness to label-flipping attacks via randomized smoothing. ICML, 2020.
  • Bagdasaryan et al. How to backdoor federated learning . ICML, 2020.
  • Sun et al. data posioning attacks on federated machine learning . IEEE ITOJ, 2021.
  • Xu et al. More is Better(mostly)_on the backdoor attacks in federated learning. ACSAC, 2022.
  • Rieger et al. Deepsight_mitigating backdoor attacks in federated learning through deep model inspection. NDSS, 2022.
  • Fang et al. On the vulnerability of backdoor defenses for federated learning. AAAI, 2023.
  • Liu et al. Poisoning_Attack_based_on_Data_Feature_Selection_in_Federated_Learning. IEEE Confluence, 2023.
  • Xu et al. Shadowcast Stealthy Data Poisoning Attacks against Vision-language Model NeurIPS, 2024.
  • Pawelczyk et al. Machine Unlearning Fails to Remove Data Poisoning Attacks arXiv, 2024.
  • Cotroneo et al. Vulnerabilities in AI Code Generators Exploring Targeted Data Poisoning Attack ICPC, 2024.
  • Yang et al. Data Poisoning Attacks Against Multimodal Encoders PMLR, 2023.
  • Zhang et al. Data_Poisoning_based_Backdoor_Attacks_to_Contrastive_Learning CVPR, 2024.
  • Cina et al. Machine Learning Security against Data Poisoning Are We There Yet arXiv, 2024.

模型投毒攻击

  • Baruch et al. a-little-is-enough-circumventing-defenses-for-distributed-learning-Paper. NeurIPS, 2019.
  • Fang et al. local model poisoning attacks to byzantine-robust federated learning. usenix, 2020.
  • Shejwalkar et al. Manipulating the byzantine_Optimizing model poisoning attacks and defense for federated learning. NDSS, 2021.
  • Cao et al. MPAF_Model_Poisoning_Attacks_to_Federated_Learning_Based_on_Fake. CVPR, 2022.
  • Zhang et al. denial-of-service or fine-grained control_towards flexible model poisoning attacks on federated learning. arxiv, 2023.
  • Zhang et al. FLTracer_accurate poisoning attack provenance in federated learning. arxiv, 2023.
  • Jin et al. FedPerturb Covert Poisoning Attack on Federated Learning via Partial Perturbation . IEEE ECAL, 2023.
  • Yuan et al. Model_Poisoning_Attack_In_Federated_Learning_Via_Adversarial_Examples. IEEE SCSET, 2023.
  • Wei et al. covert model poisoning against federated learning_algorithm design and optimization. IEEE TDSC, 2023.
  • Li et al. data-agnostic_model poisoning against federated learning_a graph autoencoder approach. IEEE TIFS, 2023.
  • Zhang et al. Denial-of-Service or Fine-Grained Control_towards FLexible model poisoning attacks on federated learning. IJCAI, 2023.
  • Jiang et al. towards efficient and certified recovery from poisoning attacks in federated learning. arxiv, 2024.
  • Aghakhani et al. TROJANPUZZLE Covertly Poisoning Code-Suggestion Models arXiv, 2024.
  • Cong et al. Test-Time Poisoning Attacks Against Test-Time Adaptiation Models arXiv, 2023.
  • Zhang et al. LoRec Large Language Model for Robust Sequential Recommendation against Poisoning Attacks arXiv, 2024.
  • Wu et al. Preference Poisoning Attacks on Reward Model Learning arXiv, 2024.
  • Zou et al. PoisonedRAG Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models arXiv, 2024.
  • Yuan et al. Robust Federated Contrastive Recommende System against Model Poisoning Attack arXiv, 2024.
  • Xie et al. Model Poisoning Attacks to Federated Learning via Multi-Round Consistency arXiv,2024.
  • Zhao et al. Models Are Codes Towards Measuring Malicious Code Poisoning Attacks on Pre-trained Model Hubs arXiv, 2024.
  • Yan et al. recess-vaccine-for-federated-learning-proactive-defense-against-model-poisoning-attacks NeurIPS, 2023.
  • Li et al. Poison Attack and Poison Detection on Deep Source Code Processing Models TOSEM, 2024.
  • Yang et al. robust-contrastive-language-image-pretraining-against-data-poisoning-and-backdoor-attacks NeurIPS, 2023.

数据投毒防御

  • Shen et al. AUROR：Defending Against Poisoning Attacks in Collaborative Deep Learning Systems. ACSAC, 2016.
  • Fung et al. Mitigating Sybils in Federated Learning Poisoning. arxiv, 2018.
  • Tolpegin et al. Data Poisoning Attacks Against Federated Learning Systems. ESORICS, 2020.
  • Fung et al. The Limitations of Federated learning. RAID, 2020.
  • Awan et al. CONTRA ：defending against Poisoning Attacks in Federated Learning. ESORICS, 2021.
  • Chen et al. De-Pois_An_Attack-Agnostic_Defense_against_Data_Poisoning_Attacks. IEEE TIFS, 2021.
  • Jebreel et al. Defending against the label-flipping attack in federated learning. arxiv, 2022.
  • Gupta et al. Long-ShortHistoryof Gradient is All You Need. ESORICS, 2022.
  • wu et al. Toward_Cleansing_Backdoored_Neural_Networks_in_Federated_Learning. ICDCS, 2022.
  • Nguyen et al. FLAME_Taming backdoors in federated learning. USENIX, 2022.
  • Rezaei et al. Run-off Election_improved Provable Defense against data poisoning attacks. arxiv, 2023.
  • Jiang et al. Data Quality Detection Mechanism Against Label Flipping Attacks in Federated Learning. IEEE TIFS, 2023.
  • Jebreel et al. FL_Defender：Combating targeted attacks in federated learning. KBS, 2023. -Ovi et al. Confident federated learning to tackle label flipped data poisoning attacks. SPIE DCS, 2023.
  • Jebreel et al. LFighter_defending against the label-flipping attack in federated learning. NN, 2024.
  • Li et al. Defend Data Poisoning Attacks on Voice Authentication arXiv, 2023.
  • Zhao et al. Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning arXiv, 2024.
  • Hong et al. Diffusion denoising as a certified defense against clean-label poisoning arXiv, 2024.
  • Gaspari et al. Have you poisoned my data defending neural networks against data poisoning arXiv, 2024.
  • Galanis et al. Defending against data poisoning attacks in federated learning via user elimination arXiv, 2024.
  • Sun et al. a gan-based data poisoning attack against federated learning systems and its countermeasure arXiv, 2024.
  • Bhat et al. PUREGEN universal data purification for train-time ppoison defense via generative model dynamics arXiv, 2024.
  • Wang et al. Defending against sophisticated poisoning attacks with rl-based aggregation in fedaerated learning arXiv, 2024.
  • Lai et al. VFedAD a defense method based on the information mechanism behind the vertical federated data poisoning attack CIKM, 2023.
  • Yang et al. Breaking state-od-the-art poisoning defenses to federated learning an optimization-based attack framework CIKM,2024.
  • Li et al. BIC-Based mixture model defense against data poisoning attacks on classifiers a comprehensive study TKDE,2024.
  • Badjie et al. Denoising Autoencoder-Based Defensive Distillation as an Adversarial Robustness Algorithm Against Data Poisoning Attacks sigada,2024.
  • Huang et al. LDPGuard_Defenses_Against_Data_Poisoning_Attacks_to_Local_Differential_Privacy_Protocols TKDE,2024.
  • Yan et al. recess-vaccine-for-federated-learning-proactive-defense-against-model-poisoning-attacks NeurIPS, 2023.
  • Wang et al. temporal-robustness-against-data-poisoning NeurIPS, 2023.
  • Rezaei et al. Run-off election improved provable defense against data poisoning attacks PMLR, 2023.

模型投毒防御

  • McMahan et al. communication-efficient learning of deep networks from decentralized data. MLR, 2016.
  • Blanchard et al. machine-learning-with-adversaries-byzantine-tolerant-gradient-descent-Paper. NIPS, 2017.
  • Muñoz-González et al. Towards poisoning of deep learning algorithms with back-gradient optimization. arxiv, 2017.
  • Yin et al. Byzantine-Robust Distributed Learning_towards optimal stastistical rates. ICML, 2018.
  • Mhamdi et al. the hidden vulnerability of distributed learning in byzantium. ICML, 2018.
  • Sun et al. can you really backdoor federated learning. arxiv, 2019.
  • Baruch et al. A little Is Enough_circumventing defense for distributed learning. NIPS, 2019.
  • Bhagoji et al. analyzing federated learning through an adversarial lens. ICML, 2019.
  • Li et al. abnormal client behavior detection in federated learning. arxiv, 2019.
  • Cao et al. Distributed_Gradient_Descent_Algorithm_Robust_to_an_Arbitrary_Number_of_Byzantine_Attackers. IEEE TOSP, 2019.
  • Xia et al. FABA_An algorithm for fast aggregation against byzantine attacks in distributed neural networks. IJCAI, 2019.
  • Pillutla et al. Robust Aggregation for Federated Learning. arxiv, 2019.
  • Li et al. RSAByzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets. AAAI, 2019.
  • Liu et al. communication-efficient federated learning for anomaly detection in industrial internet of things. IEEE GB, 2020.
  • Sun et al. Data poisoning attacks on federated machine learning. arxiv, 2020.
  • Li et al. learning to detect malicious clients for robust federated learning. arxiv, 2020.
  • Sun et al. fl-wbc-enhancing-robustness-against-model-poisoning-attacks-in-federated-learning-from-a-client-perspective-Paper (1). NeurIPS, 2021.
  • Cao et al. FLTRUST ：Byzantine-robust federated learning via trust bootstrapping. NDSS, 2021.
  • Shejwalkar et al. Manipulating the byzantine_optimizing model poisoning attacks and defenses for federated learning. NDSS, 2021.
  • Xu et al. Byzantine-robost Federated learning through Collaborative Malicious Gradient Filtering. ICDCS, 2022.
  • Wang et al. Federated unlearning via class-discriminative pruning. WWW, 2022.
  • Cao et al. FLCert_Provably_Secure_Federated_Learning_Against_Poisoning_Attacks. IEEE TIFS, 2022.
  • Zhang et al. FLDetector_defending federated learning against model poisoning attacks via detecting malicious clients. KDD, 2022.
  • Weng et al. Privacy-Preserving_Federated_Learning_based_on_Differential_Privacy_and_Momentum_Gradient_Descent. IJCNN, 2022.
  • Shejwalkar et al. Back to the Drawing Board： A critical Evaluation of poisoning attacks on production federated learning. IEEE-SP, 2022.
  • Panda et al. SparseFed_mitigating model poisoning attacks in federated learning with sparsification. AISTATS, 2022.
  • Gong et al. agramplifier_defending federated learning agaginst poisoning attacks through local update amplification. IEEE TIFS, 2023.
  • Yin et al. Defending_Against_Data_Poisoning_Attack_in_Federated_Learning_With_Non-IID_Data. IEEE TOCSS, 2023.
  • Aloran et al. Defending_Federated_Learning_Against_Model_Poisoning_Attacks. IEEE BigData, 2023.
  • Yan et al. DeFL_defending against model poisoning attacks in federated learning via critical learning periods awareness. AAAI, 2023.
  • Lu et al. Depriving_the_Survival_Space_of_Adversaries_Against_Poisoned_Gradients_in_Federated_Learning. IEEE TIFS, 2023.
  • Mozaffari et al. every vote count_ranking-based training of federated learning to resist poisoning attacks. usenix, 2023.
  • Guo et al. FAST_Adopting_Federated_Unlearning_to_Eliminating_Malicious_Terminals_at_Server_Side. IEEE TNSE, 2023.
  • Zhao et al. FedCom_Byzantine-Robust_Federated_Learning_Using_Data_Commitment. IEEE ICC, 2023.
  • Chelli et al. FedGuard_Selective_Parameter_Aggregation_for_Poisoning_Attack_Mitigation_in_Federated_Learning. IEEE CLUSTER, 2023.
  • Zhang et al. FedRecovery_Differentially_Private_Machine_Unlearning_for_Federated_Learning_Frameworks. IEEE TIFS, 2023.
  • Cao et al. FedRecover_Recovering_from_Poisoning_Attacks_in_Federated_Learning_using_Historical_Information. IEEE SP, 2023.
  • Ebron Jr. et al. FedTruth_byzantine_robust and backdoor-resilient federated learning framework. arxiv, 2023.
  • Liu et al. FLOW_A_Robust_Federated_Learning_Framework_to_Defend_Against_Model_Poisoning_Attacks_in_IoTs. IEEE IOTJ, 2023.
  • Han et al. kick bad guys out zero-knowledge-proof-based anomaly detection in federated learning. arxiv, 2023.
  • Zhu et al. leadFL_Client self-defense against model poisoning in federated learning. PMLR, 2023.
  • Li et al. loMar_a local defense against poisoning attack on federated learning . IEEE TDSC, 2023.
  • Krauß et al. MESAS_poisoning defense for federated learning resilient against adaptive attacks. CCS, 2023.
  • Yan et al. reces vaccine for federated learning_proactive defense against model poisoning attacks. arxiv, 2023.
  • Yaldiz et al. secure federated learning against modek=l poisonong attacks via client filtering. ICLR, 2023.
  • Zhao et al. Semisupervised_Federated-Learning-Based_Intrusion_Detection_Method_for_Internet_of_Things. IEEE IOTJ, 2023.
  • Shen et al. SPEFL_Efficient_Security_and_Privacy_Enhanced_Federated_Learning_Against_Poisoning_Attacks. IEEE IOTJ, 2023.
  • Ma et al. 面向Non-IID数据的拜占庭鲁棒联邦学习. 通信学报, 2023.
  • Yang et al. A_Robust_and_Efficient_Federated_Learning_Algorithm_Against_Adaptive_Model_Poisoning_Attacks. IEEE ITOJ, 2024.
  • Dong et al. FADngs_Federated_Learning_for_Anomaly_Detection. IEEE TNNLS, 2024.
  • Han et al. kick bad guys out_zero-knowlege-proof-based anomaly detection in federated learning. ICLR, 2024.
  • Yang et al. RoseAgg_Robust_Defense_against_Targeted_Collusion_Attacks_in_Federated_Learning. IEEE TIFS, 2024.
  • Kasyap et al. Sine_Similarity_is_not_enough_for_mitigating_Local_Model_Poisoning_Attacks_in_Federated_Learning. IEEE TDSC, 2024.
  • Huang et al. VPPFL_A verifiable privacy- preserving federated learning scheme against poisoning attacks. CS, 2024.
  • Kabir et al. FLShield a validation based federated learning framework to defend against poisoning attacks arXiv, 2023.
  • Han et al. FedMID a data-free method for using intermediate outputs as a defense mechanism against poisoning attacks in federated learning arXiv, 2024.
  • Zhao et al. Models are codes towards measuring malicious code poisoning attacks on pre-trained model hubs arXiv,2024.
  • Xie et al. FedREDefense defending against model poisoning attacks for federated learning using model update reconstruction error PMLR, 2024.
  • Chen et al. Tutorial toward robust deep learning against poisoning attacks TECS, 2023.
  • Krau et al. MESAS poisoning defense for federated learning resilient against adaptive attackers CCS,2023.
  • Sharma et al. FLAIR defense against model poisoning attack in federated learning , 2023.
  • Zheng et al. detecting poisoning attacks on federated learning using gradient-weighted class activation mapping WWW, 2024.
  • Yang et al. A_Robust_and_Efficient_Federated_Learning_Algorithm_against_Adaptive_Model_Poisoning_Attacks IoT, 2024.
  • Zhou et al. Inversion-Guided_Defense_Detecting_Model_Stealing_Attacks_by_Output_Inverting TIFS, 2024.
  • Wang et al. defending-against-data-free-model-extraction-by-distributionally-robust-defensive-training NeurIPS, 2023.
  • Zhang et al. Poisoning-Free_Defense_Against_Black-Box_Model_Extraction ICASSP, 2024
  • Zhu et al. LeadFL client self-defense against model poisoning in federated learning PMLR, 2024
  • Zhang et al. Poisoning Free Defense Against Black Box Model Extraction ICASSP, 2024.
  • Yan et al. DeFL defending against model poisoning attacks in federated learning via critical learning periods awareness AAAI, 2023.
  • Zhu et al. detection and defense of unlearnable examples AAAI, 2024.

隐私保护的联邦学习

  • Liu et al. 联邦学习中的隐私保护技术. 软件学报, 2021.

隐私保护的联邦推荐

  • Zhang et al. 基于隐私保护的联邦推荐算法综述_张洪磊. 自动化学报, 2022.
  • Xue et al. Advanced_Privacy-Preserving_Federated_Relationship_Recommendation. IEEE ICAICA, 2023.

联邦推荐中的现有攻击

  • Chen et al. Robust Federated Recommendation System. arxiv, 2020.
  • Huang et al. data poisoning attacks to deep learning based recommender systems. NDSS, 2021.
  • Rong et al. FedRecAttack Model Poisoning Attack to Federated Recommendation. IEEE ICDE, 2022.
  • Zhang et al. PipAttack Poisoning Federated Recommender Systems for. arxiv, 2022.
  • Wu et al. Fedattack_effectivate and covert poisoning attack on federated recommendation via hard sampling. kdd, 2022.
  • Yu et al. Untargeted attack against federated recommendation systems via poisonous item embeddings and the defense. AAAI, 2023.

联邦推荐中的现有防御

  • Sandeepa et al. Rec-Def_A_Recommendation-Based_Defence_Mechanism_for_Privacy_Preservation_in_Federated_Learning_Systems. IEEE TCE, 2024.

一些开源代码

日期 2017年前 2017-2019 2020 2021 2022 2023 2024
论文数量 3 14 10 11 24 60 46
总计 168