Awesome Adversarial Attacks

Key papers

• Federated Adversarial Domain Adaptation (2019)
  Xingchao Peng et al.
  10.99
• Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning (2020)
  Xinjian Luo et al.
  6.66
• Do We Really Need to Design New Byzantine-robust Aggregation Rules? (2025)
  Minghong Fang et al.
  6.58
• Energy-Latency Attacks via Sponge Poisoning (2022)
  Antonio Emanuele Cin\`a et al.
  5.72
• FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks (2022)
  Lingzhi Gao et al.
  5.06
• Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities (2024)
  Zikai Zhang et al.
  4.54
• Federated Multi-Armed Bandits Under Byzantine Attacks (2022)
  Artun Saday et al.
  4.48
• Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity (2024)
  Hao Jian Huang et al.
  4.41
• AI Security Research Should Better Incentivize Defense Research (2026)
  Youqian Zhang
  4.33
• Multi-Factor Trust-Driven Secure Communication Model for Cloud-Based Digital Twins (2026)
  Deepika Saxena et al.
  4.33
• When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech (2026)
  Qingwen Zeng et al.
  4.33
• BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning (2024)
  Zhengyuan Jiang et al.
  4.30
• Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks (2025)
  Ebtisaam Alharbi et al.
  4.30
• Model Poisoning Attacks to Federated Learning via Multi-Round Consistency (2024)
  Yueqi Xie et al.
  4.26
• FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks (2025)
  Jian Chen et al.
  4.25
• CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling (2025)
  Kaiyuan Zhang et al.
  4.25
• SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces (2026)
  Tianwang Jia et al.
  3.98
• FL-Defender: Combating Targeted Attacks in Federated Learning (2022)
  Najeeb Jebreel and Josep Domingo-Ferrer
  3.71
• Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks (2025)
  Pengxin Guo et al.
  3.70
• Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning (2025)
  Runhua Xu et al.
  3.64
• Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning (2025)
  Mingyuan Fan et al.
  3.59
• Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions (2024)
  Hao Du et al.
  3.53
• From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning (2026)
  Huong Nguyen et al.
  3.51
• A Unified Framework for Gradient Aggregation in Multi-Objective Optimization (2026)
  Zeou Hu et al.
  3.45
• Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning (2024)
  Shihua Sun et al.
  3.25
• LOGSAFE: Logic-Guided Verification for Trustworthy Federated Time-Series Learning (2024)
  Dung Thuy Nguyen et al.
  3.20
• Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios (2022)
  Dazhong Rong et al.
  3.19
• Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning (2022)
  Sayak Mukherjee et al.
  3.19
• The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape (2024)
  Joshua C. Zhao et al.
  3.14
• Hammer and Anvil: A Principled Defense Against Backdoors in Federated Learning (2025)
  Lucas Fenaux et al.
  3.10
• Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks (2025)
  Ngoc Duy Pham et al.
  2.99
• FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning (2024)
  Gongxi Zhu et al.
  2.97
• Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning (2023)
  Gergely D\'aniel N\'emeth et al.
  2.81
• Approaching the Harm of Gradient Attacks While Only Flipping Labels (2025)
  Abdessamad El-Kabid et al.
  2.76
• SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning (2025)
  Heyi Zhang et al.
  2.71
• The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning (2025)
  Kunlan Xiang et al.
  2.71
• Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated Learning (2025)
  Mario Garc\'ia-M\'arquez and Nuria Rodr\'iguez-Barroso and M.Victoria Luz\'on and Francisco Herrera
  2.71
• DROP: Poison Dilution via Knowledge Distillation for Federated Learning (2025)
  Georgios Syros et al.
  2.71
• Secure Federated Data Distillation (2025)
  Marco Arazzi et al.
  2.71
• TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning (2025)
  Ehsan Hallaji et al.
  2.71
• Mingling with the Good to Backdoor Federated Learning (2025)
  Nuno Neves
  2.65
• From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning (2025)
  Chao Feng et al.
  2.65
• Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective (2025)
  Zhongjian Zhang et al.
  2.65
• TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning (2025)
  Runhua Xu et al.
  2.65
• KeTS: Kernel-based Trust Segmentation against Model Poisoning Attacks (2025)
  Ankit Gangwal et al.
  2.65
• Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning (2025)
  Seohyun Lee et al.
  2.65
• Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning (2025)
  Mustafa Ghaleb et al.
  2.65
• DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning (2025)
  Bohan Liu et al.
  2.65
• Poisoning Attacks and Defenses to Federated Unlearning (2025)
  Wenbin Wang et al.
  2.65
• FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning (2023)
  Yanxin Yang et al.
  2.64
• DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning (2024)
  Kichang Lee et al.
  2.54
• Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications (2022)
  Ali Raza et al.
  2.53
• WW-FL: Secure and Private Large-Scale Federated Learning (2023)
  Felix Marx et al.
  2.53
• Advancing Hybrid Defense for Byzantine Attacks in Federated Learning (2024)
  Kai Yue et al.
  2.43
• Byzantine-Robust Aggregation for Securing Decentralized Federated Learning (2024)
  Diego Cajaraville-Aboy et al.
  2.43
• Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges (2024)
  Yanli Li et al.
  2.32
• Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning (2024)
  Yuqi Jia and Minghong Fang and Hongbin Liu and Jinghuai Zhang and Neil Zhenqiang Gong
  2.32
• UIFV: Data Reconstruction Attack in Vertical Federated Learning (2024)
  Jirui Yang et al.
  2.26
• Robust Decentralized Learning with Local Updates and Gradient Tracking (2024)
  Sajjad Ghiasvand et al.
  2.21
• SoK: On Gradient Leakage in Federated Learning (2024)
  Jiacheng Du et al.
  2.15