Key papers

• Privacy Risks Of Securing Machine Learning Models Against Adversarial Examples (2019)
  Liwei Song, Reza Shokri, Prateek Mittal
  16.71
• Deep K-nn Defense Against Clean-label Data Poisoning Attacks (2019)
  Neehar Peri, Neal Gupta, W. Ronny Huang, et al.
  16.44
• Provable Defense Against Privacy Leakage In Federated Learning From Representation Perspective (2020)
  Jingwei Sun, Ang Li, Binghui Wang, et al.
  16.21
• Enhanced Membership Inference Attacks Against Machine Learning Models (2021)
  Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, et al.
  15.78
• Eluding Secure Aggregation In Federated Learning Via Model Inconsistency (2021)
  Dario Pasquini, Danilo Francati, Giuseppe Ateniese
  14.58
• Byzantine-resilient Decentralized Stochastic Gradient Descent (2020)
  Shangwei Guo, Tianwei Zhang, Han Yu, et al.
  13.70
• Byzantine-robust Federated Learning Through Collaborative Malicious Gradient Filtering (2021)
  Jian Xu, Shao-Lun Huang, Linqi Song, et al.
  13.70
• Quantifying Privacy Risks Of Masked Language Models Using Membership Inference Attacks (2022)
  Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, et al.
  13.11
• Time-aware Gradient Attack On Dynamic Network Link Prediction (2019)
  Jinyin Chen, Jian Zhang, Zhi Chen, et al.
  11.08
• Baybfed: Bayesian Backdoor Defense For Federated Learning (2023)
  Kavita Kumari, Phillip Rieger, Hossein Fereidooni, et al.
  10.48
• Formalizing And Estimating Distribution Inference Risks (2021)
  Anshuman Suri, David Evans
  10.48
• Federated Robustness Propagation: Sharing Robustness In Heterogeneous Federated Learning (2021)
  Junyuan Hong, Haotao Wang, Zhangyang Wang, et al.
  10.07
• Privacy Inference-empowered Stealthy Backdoor Attack On Federated Learning Under Non-iid Scenarios (2023)
  Haochen Mei, Gaolei Li, Jun Wu, et al.
  9.92
• Semi-targeted Model Poisoning Attack On Federated Learning Via Backward Error Analysis (2022)
  Yuwei Sun, Hideya Ochiai, Jun Sakuma
  9.92
• Backdoor Attacks In Federated Learning By Rare Embeddings And Gradient Ensembling (2022)
  Kiyoon Yoo, Nojun Kwak
  9.76
• Zprobe: Zero Peek Robustness Checks For Federated Learning (2022)
  Zahra Ghodsi, Mojan Javaheripi, Nojan Sheybani, et al.
  9.59
• Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey (2025)
  Wencheng Yang et al.
  9.50
• Feddrl: A Trustworthy Federated Learning Model Fusion Method Based On Staged Reinforcement Learning (2023)
  Leiming Chen, Weishan Zhang, Cihao Dong, et al.
  9.41
• Federated And Transfer Learning: A Survey On Adversaries And Defense Mechanisms (2022)
  Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
  9.23
• Denial-of-service Or Fine-grained Control: Towards Flexible Model Poisoning Attacks On Federated Learning (2023)
  Hangtao Zhang, Zeming Yao, Leo Yu Zhang, et al.
  9.23
• ARFED: Attack-resistant Federated Averaging Based On Outlier Elimination (2021)
  Ece Isik-Polat, Gorkem Polat, Altan Kocyigit
  8.82
• Dropout Is NOT All You Need To Prevent Gradient Leakage (2022)
  Daniel Scheliga, Patrick Mäder, Marco Seeland
  8.82
• Influence Based Defense Against Data Poisoning Attacks In Online Learning (2021)
  Sanjay Seetharaman, Shubham Malaviya, Rosni Kv, et al.
  8.82
• Covert Model Poisoning Against Federated Learning: Algorithm Design And Optimization (2021)
  Kang Wei, Jun Li, Ming Ding, et al.
  8.82
• Blockchain-empowered Cyber-secure Federated Learning For Trustworthy Edge Computing (2024)
  Ervin Moore, Ahmed Imteaj, Md Zarif Hossain, et al.
  8.09
• Have You Poisoned My Data? Defending Neural Networks Against Data Poisoning (2024)
  Fabio de Gaspari, Dorjan Hitaj, Luigi V. Mancini
  7.81
• PACE: Poisoning Attacks On Learned Cardinality Estimation (2024)
  Jintao Zhang, Chao Zhang, Guoliang Li, et al.
  7.50
• Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning (2025)
  Ze Yang et al.
  7.02
• Bfclass: A Backdoor-free Text Classification Framework (2021)
  Zichao Li, Dheeraj Mekala, Chengyu Dong, et al.
  6.77
• Dissecting Distribution Inference (2022)
  Anshuman Suri, Yifu Lu, Yanjin Chen, et al.
  6.77
• Detecting Backdoor Attacks In Federated Learning Via Direction Alignment Inspection (2025)
  Jiahao Xu, Zikai Zhang, Rui Hu
  6.58
• Aspis: Robust Detection For Distributed Learning (2021)
  Konstantinos Konstantinidis, Aditya Ramamoorthy
  6.34
• Detection And Mitigation Of Byzantine Attacks In Distributed Training (2022)
  Konstantinos Konstantinidis, Namrata Vaswani, Aditya Ramamoorthy
  6.34
• A Cyber Threat Intelligence Sharing Scheme Based On Federated Learning For Network Intrusion Detection (2021)
  Mohanad Sarhan, Siamak Layeghy, Nour Moustafa, et al.
  6.34
• Robust And Efficient Aggregation For Distributed Learning (2022)
  Stefan Vlaski, Christian Schroth, Michael Muma, et al.
  6.34
• Securing RAG: A Risk Assessment and Mitigation Framework (2025)
  Lukas Ammann et al.
  6.01
• Membership Inference Attacks Against Temporally Correlated Data In Deep Reinforcement Learning (2021)
  Maziar Gomrokchi, Susan Amin, Hossein Aboutalebi, et al.
  5.84
• Krum Federated Chain (KFC): Using Blockchain To Defend Against Adversarial Attacks In Federated Learning (2025)
  Mario García-Márquez, Nuria Rodríguez-Barroso, M. Victoria Luzón, et al.
  5.40
• Fedsv: Byzantine-robust Federated Learning Via Shapley Value (2025)
  Khaoula Otmani, Rachid Elazouzi, Vincent Labatut
  5.40
• Self-Supervised Federated GNSS Spoofing Detection with Opportunistic Data (2025)
  Wenjie Liu and Panos Papadimitratos
  5.35
• Aijack: Let's Hijack AI! Security And Privacy Risk Simulator For Machine Learning (2023)
  Hideaki Takahashi
  5.25
• Backdoor Attacks In Peer-to-peer Federated Learning (2023)
  Georgios Syros, Gokberk Yar, Simona Boboila, et al.
  5.24
• Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks (2025)
  Ebtisaam Alharbi et al.
  5.18
• Towards Privacy-Preserving Split Learning: Destabilizing Adversarial Inference and Reconstruction Attacks in the Cloud (2025)
  Griffin Higgins et al.
  5.18
• SoK: Attack and Defense Landscape of Mobile On-device AI Systems (2026)
  Yujin Huang et al.
  5.01
• Embedding Inference Attack (2026)
  Cedric Fitiavana Raelijohn et al.
  5.01
• Generative AI and Federated Learning for Intrusion Detection Systems: A Survey (2026)
  Jiefei Liu et al.
  5.01
• Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies (2026)
  Barkha Rani
  5.01
• Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data (2026)
  Vin\'icius Gabriel Angelozzi et al.
  5.01
• Privacy Preserving Recommender Systems Balancing Personalization with Privacy (2026)
  Ranjeet K Jha et al.
  5.01
• ReBound: Reuse-Aware Privacy For Interactive Decision Support (2026)
  Nada Lahjouji et al.
  5.01
• PriEval-Protect: A Unified Framework for Privacy Evaluation and Protection in Healthcare Systems (2026)
  Ilef Chebil et al.
  5.01
• Plausible Deniability Guarantees for Whistleblowers (2026)
  Leo Richter et al.
  5.01
• A Queueing-Stability Criterion for Causal IPD-QIM Network Flow Watermarking (2026)
  Jiuxiang Cao et al.
  5.01
• Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification (2026)
  Carter Luck et al.
  5.01
• Decentralized Compute on Untrusted Hardware Using Intel TDX and Encrypted CVMs (2026)
  Venish Patidar et al.
  5.01
• PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption (2026)
  Liangqin Ren et al.
  5.01
• Agent Security Needs Redefinition through a Holistic Framework (2026)
  Vincent Siu et al.
  5.01
• Exponentially Fewer-Server PIR from Sparser $S$-Decoding Polynomials (2026)
  Aparna Gupte et al.
  5.01
• BioZKFHE: Scalable Encrypted Biometric Identification via Verifiable Homomorphic Similarity Evaluation (2026)
  Rundong Xin et al.
  5.01