Key papers

• Federated Adversarial Domain Adaptation (2019)
  Xingchao Peng et al.
  10.99
• A Survey on Privacy Risks and Protection in Large Language Models (2025)
  Kang Chen et al.
  8.23
• FederBoost: Private Federated Learning for GBDT (2020)
  Zhihua Tian et al.
  8.19
• Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning (2025)
  Mohammad Khalil et al.
  7.19
• Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning (2025)
  Ze Yang et al.
  7.02
• Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning (2020)
  Xinjian Luo et al.
  6.66
• Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach (2025)
  Atit Pokharel et al.
  6.64
• Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification (2025)
  Gazi Tanbhir and Md. Farhan Shahriyar
  6.63
• Personalized Semi-Supervised Federated Learning for Human Activity Recognition (2021)
  Riccardo Presotto et al.
  6.53
• Shadow defense against gradient inversion attack in federated learning (2025)
  Le Jiang et al.
  6.34
• Differentially Private Federated Learning: A Systematic Review (2024)
  Jie Fu et al.
  6.33
• Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise (2025)
  Ratun Rahman et al.
  6.12
• Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case (2025)
  William Marfo et al.
  6.06
• Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology (2025)
  Yanjie Wu et al.
  5.96
• PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark (2023)
  Jianqing Zhang et al.
  5.86
• Enhancing Air Quality Monitoring: A Brief Review of Federated Learning Advances (2025)
  Sara Yarham et al.
  5.65
• Enhancing Smart Farming Through Federated Learning: A Secure, Scalable, and Efficient Approach for AI-Driven Agriculture (2025)
  Ritesh Janga and Rushit Dave
  5.57
• Federated Detection of Open Charge Point Protocol 1.6 Cyberattacks (2025)
  Christos Dalamagkas et al.
  5.54
• Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions (2025)
  Youngjoon Lee et al.
  5.54
• Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems (2025)
  Pavle Vasiljevic et al.
  5.40
• Towards Privacy-Preserving Mental Health Support with Large Language Models (2026)
  Dong Xue et al.
  5.25
• Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks (2025)
  Ebtisaam Alharbi et al.
  5.18
• ORQ: Complex Analytics on Private Data with Strong Security Guarantees (2025)
  Eli Baum et al.
  5.15
• AFed: Algorithmic Fair Federated Learning (2025)
  Huiqiang Chen et al.
  5.13
• Unlearning Clients, Features and Samples in Vertical Federated Learning (2025)
  Ayush K. Varshney et al.
  5.13
• FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks (2022)
  Lingzhi Gao et al.
  5.06
• Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources (2025)
  Roopkatha Banerjee et al.
  5.04
• Federated Transfer Learning with Differential Privacy (2024)
  Mengchu Li et al.
  5.02
• 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
• Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning (2026)
  Jiayuan Ding et al.
  5.01
• Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets (2026)
  Rodrigo Tertulino et al.
  5.01
• Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems (2024)
  Ayoub Si-ahmed et al.
  4.98
• A User-Centric, Privacy-Preserving, and Verifiable Ecosystem for Personal Data Management and Utilization (2025)
  Osama Zafar et al.
  4.98
• Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks (2025)
  Pengxin Guo et al.
  4.98
• Local Differential Privacy with Correlated Noise Achieves Central-DP Optimal Cost (2026)
  Madhura Pathegama et al.
  4.90
• Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction (2025)
  Yazan Otoum et al.
  4.87
• Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks (2025)
  Pengxin Guo et al.
  4.82
• Privacy-Preserved Automated Scoring using Federated Learning for Educational Research (2025)
  Ehsan Latif and Xiaoming Zhai
  4.82
• PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning (2025)
  Yu Feng et al.
  4.76
• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks (2025)
  Shuai Wang et al.
  4.71
• Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review (2024)
  Luis M. Lopez-Ramos et al.
  4.65
• Differentially Private Federated Quantum Learning via Quantum Noise (2025)
  Atit Pokharel et al.
  4.58
• Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities (2024)
  Zikai Zhang et al.
  4.54
• Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks (2023)
  Payam Abdisarabshali et al.
  4.52
• Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning (2025)
  Hasin Us Sami et al.
  4.47
• A Federated Random Forest Solution for Secure Distributed Machine Learning (2025)
  Alexandre Cotorobai et al.
  4.42
• Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity (2024)
  Hao Jian Huang et al.
  4.41
• Federated Sovereign Transport Protocol (FSTP): Verifiable Coordination Without Disclosure (2026)
  Ram\'on Soto C. (Department of Accounting et al.
  4.39
• Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification (2026)
  Aizierjiang Aiersilan
  4.39
• Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning (2026)
  Haemin Park et al.
  4.39
• Privacy-Preserving and Verifiable Approximate Distributed Coded Computing (2026)
  Xavier Mart\'inez-Lua\~na et al.
  4.39
• Securing People and their Machines Against Major Faults (2026)
  Ohad Eitan et al.
  4.39
• QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition (2026)
  Quoc Bao Phan et al.
  4.39
• Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework (2026)
  Xunkai Li et al.
  4.39
• SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework (2026)
  Akarsh K Nair et al.
  4.39
• Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction (2026)
  Ruth Amey et al.
  4.39
• A Drift Stable Quantum Federated Learning for Intelligent Services (2026)
  Shanika Iroshi Nanayakkara et al.
  4.39
• HiTMS: A High-Throughput Multi-Stream Linguistic Steganography Framework (2026)
  Ruiyi Yan et al.
  4.39
• I2VShield: An Efficient Proactive Defense Framework against DiT-based Image-to-Video Models (2026)
  Yimao Guo et al.
  4.39