Key papers

• Federated Adversarial Domain Adaptation (2019)
  Xingchao Peng et al.
  10.99
• Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning (2025)
  Mohammad Khalil et al.
  7.19
• Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning (2020)
  Xinjian Luo et al.
  6.66
• 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
• Generative AI and Federated Learning for Intrusion Detection Systems: A Survey (2026)
  Jiefei Liu et al.
  5.01
• Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustness (2026)
  Thomas Boudou et al.
  5.01
• Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks (2025)
  Pengxin Guo et al.
  4.98
• Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks (2025)
  Pengxin Guo et al.
  4.82
• Differentially Private Federated Quantum Learning via Quantum Noise (2025)
  Atit Pokharel et al.
  4.58
• Federated Multi-Armed Bandits Under Byzantine Attacks (2022)
  Artun Saday et al.
  4.48
• Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning (2025)
  Hasin Us Sami et al.
  4.47
• Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity (2024)
  Hao Jian Huang et al.
  4.41
• Privacy-Preserving and Verifiable Approximate Distributed Coded Computing (2026)
  Xavier Mart\'inez-Lua\~na et al.
  4.39
• ChannelGuard: Safe Models Do Not Compose into Safe Multi-Agent Systems (2026)
  Elias Hossain et al.
  4.39
• Integrity of peer-to-peer distributed LLM inference under malicious nodes (2026)
  Mert Cihangiroglu et al.
  4.39
• FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defense (2026)
  Chenyu Zhou et al.
  4.39
• Chained Attacks on Drone-Based Federated Learning: From Network Disruption to Device Impersonation (2026)
  Suleiman Muhammad Sabo et al.
  4.39
• Where Is the Cost of Third-Party API Routers in Agentic Software Development? (2026)
  Donghao Fu et al.
  4.39
• Explanation-Bound Tool Execution for AI Agents: Server-Verified Action Claims Without Trusting Model Rationales (2026)
  Genliang Zhu (Accentrust et al.
  4.39
• I2VShield: An Efficient Proactive Defense Framework against DiT-based Image-to-Video Models (2026)
  Yimao Guo et al.
  4.39
• Decoupling Trust in Byzantine CRDTs: Fine-grained Post-Compromise Handling without Breaking Causality (2026)
  Amos Brocco
  4.33
• AI Security Research Should Better Incentivize Defense Research (2026)
  Youqian Zhang
  4.27
• Multi-Factor Trust-Driven Secure Communication Model for Cloud-Based Digital Twins (2026)
  Deepika Saxena and Ashutosh Kumar Singh
  4.27
• When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech (2026)
  Qingwen Zeng et al.
  4.27
• Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks (2025)
  Yohannis Kifle Telila et al.
  4.25
• FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks (2025)
  Jian Chen et al.
  4.19
• The Role of Federated Learning in Improving Financial Security: A Survey (2025)
  Cade Houston Kennedy et al.
  4.03
• Byzantine Machine Learning: MultiKrum and an optimal notion of robustness (2026)
  Gilles Bareilles et al.
  3.98
• Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats (2025)
  Chaoyu Zhang and Heng Jin and Shanghao Shi and Hexuan Yu and Sydney Johns and Y. Thomas Hou and Wenjing Lou
  3.97
• Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments (2025)
  Saeid Sheikhi et al.
  3.97
• Label Inference Attacks against Federated Unlearning (2025)
  Wei Wang et al.
  3.92
• SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces (2026)
  Tianwang Jia et al.
  3.92
• Proof of Reasoning for Privacy Enhanced Federated Blockchain Learning at the Edge (2026)
  James Calo and Benny Lo
  3.92
• Res-MIA: A Training-Free Resolution-Based Membership Inference Attack on Federated Learning Models (2026)
  Mohammad Zare and Pirooz Shamsinejadbabaki
  3.92
• 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
  3.59
• FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels (2026)
  Md Nahid Hasan Shuvo et al.
  3.51
• From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning (2026)
  Huong Nguyen et al.
  3.45
• Federated Anomaly Detection and Mitigation for EV Charging Forecasting Under Cyberattacks (2025)
  Oluleke Babayomi et al.
  3.15
• Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning (2025)
  Lucas Fenaux et al.
  3.04
• WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems (2025)
  Sameera K. M. et al.
  2.76
• Approaching the Harm of Gradient Attacks While Only Flipping Labels (2025)
  Abdessamad El-Kabid et al.
  2.71
• The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning (2025)
  Kunlan Xiang 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.
  2.60
• Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications (2022)
  Ali Raza et al.
  2.53
• ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D (2026)
  Lena Libon et al.
  2.00
• On Exercising Governance Power in Decentralized Autonomous Organizations (2026)
  Vabuk Pahari et al.
  2.00
• Recover, Decode, Reguard: Guard-Agnostic Defense Amplification againstEncoded VLM Jailbreaks (2026)
  Haoyu Zhang et al.
  2.00
• Borrowed Strength: Best-of-N Search over a Code EncodingBreaks Self-Check Jailbreak Defenses (2026)
  Haoyu Zhang et al.
  2.00
• Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning (2026)
  Hongliang Zhang et al.
  2.00
• On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment (2026)
  Yongjian Guo et al.
  2.00
• Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning (2026)
  Ivo Osterberg Nilsson et al.
  1.94
• SECUREVENT: Hybrid AI/ML Security Monitoring for Distributed Event-Based Systems (2026)
  Eric Liang
  1.94
• IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning (2026)
  Farhin Farhad Riya et al.
  1.94
• DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning (2026)
  Guanlong Wu et al.
  1.94
• PoCQ: Proof of Contribution Quality as a Lightweight Blockchain Consensus for Secure Federated Learning (2026)
  Sudad Abed et al.
  1.94
• Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation (2026)
  Qiaoyu Chen et al.
  1.94
• CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learning (2026)
  Yongqi Jiang et al.
  1.94
• Quantifying and Defending against the Privacy Risk in Logit-based Federated Learning (2026)
  Sheng Wan et al.
  1.94
• Model Poisoning Against Federated Model Adaptation with Chain of Bit-Flips (2026)
  Bastien Vuillod et al.
  1.94