Awesome Privacy

Key papers

• Federated Adversarial Domain Adaptation (2019)
  Xingchao Peng et al.
  10.99
• A Survey on Decentralized Federated Learning (2023)
  Edoardo Gabrielli et al.
  10.32
• FederBoost: Private Federated Learning for GBDT (2020)
  Zhihua Tian et al.
  8.19
• Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data (2021)
  Zilong Zhao et al.
  7.50
• Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning (2020)
  Xinjian Luo et al.
  6.66
• Personalized Semi-Supervised Federated Learning for Human Activity Recognition (2021)
  Riccardo Presotto et al.
  6.53
• Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy (2026)
  Emre Ardıç et al.
  6.52
• Differentially Private Federated Learning: A Systematic Review (2024)
  Jie Fu et al.
  6.33
• Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case (2025)
  William Marfo et al.
  6.12
• PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark (2023)
  Jianqing Zhang et al.
  5.86
• Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications (2023)
  Francesco Cremonesi et al.
  5.30
• AFed: Algorithmic Fair Federated Learning (2025)
  Huiqiang Chen et al.
  5.18
• Unlearning Clients, Features and Samples in Vertical Federated Learning (2025)
  Ayush K. Varshney et al.
  5.18
• FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks (2022)
  Lingzhi Gao et al.
  5.06
• Federated Transfer Learning with Differential Privacy (2024)
  Mengchu Li et al.
  5.02
• Post-Quantum Secure Federated DeFi for Inclusive Banking (2026)
  Swati Sachan 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
• Local Differential Privacy with Correlated Noise Achieves Central-DP Optimal Cost (2026)
  Madhura Pathegama et al.
  4.95
• Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction (2025)
  Yazan Otoum et al.
  4.93
• PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning (2025)
  Yu Feng et al.
  4.82
• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks (2025)
  Shuai Wang et al.
  4.76
• Distributed Intrusion Detection in Dynamic Networks of UAVs using Few-Shot Federated Learning (2025)
  Ozlem Ceviz et al.
  4.76
• Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review (2024)
  Luis M. Lopez-Ramos et al.
  4.65
• Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities (2024)
  Zikai Zhang et al.
  4.54
• Federated Large Language Models: Current Progress and Future Directions (2024)
  Yuhang Yao 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
• Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity (2024)
  Hao Jian Huang et al.
  4.41
• QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning (2026)
  Nazmus Shakib Shadin et al.
  4.39
• MoE Enhanced Federated Learning for Spatiotemporal Prediction (2026)
  Zhehao Dai et al.
  4.39
• Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping (2023)
  Martin Pelikan et al.
  4.35
• Intercloud: Eventual Consistency for Decentralised Economies via Chilling-Effect Consensus (2026)
  Gregory Magarshak
  4.33
• FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning (2026)
  Rachid Hedjam
  4.33
• Building a privacy-preserving Federated Recommender system for mobile devices (2026)
  Aasheesh Singh
  4.33
• FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data (2026)
  Maryam Moradpour et al.
  4.33
• Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks (2026)
  Zhishuai Guo et al.
  4.33
• Image Feature Fusion-based Federated Client Unlearning (FCU) (2026)
  Hangyi Shen et al.
  4.33
• Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences (2026)
  Jabin Koo et al.
  4.33
• DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs (2026)
  Ashley Hoi-Ting Au et al.
  4.33
• Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks (2025)
  Ebtisaam Alharbi et al.
  4.30
• Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation (2025)
  Xiaoxiao He et al.
  4.25
• Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deployment (2025)
  Xubin Wang et al.
  4.25
• Artificial Intelligence-Driven Clinical Decision Support Systems (2025)
  Muhammet Alkan et al.
  4.25
• FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks (2025)
  Jian Chen et al.
  4.25
• A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning (2025)
  Abdulkadir Korkmaz and Praveen Rao
  4.25
• CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling (2025)
  Kaiyuan Zhang et al.
  4.25
• Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing (2025)
  Minghong Fang et al.
  4.25
• Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning (2025)
  Yu Jiang et al.
  4.20
• FedICT: Federated Multi-task Distillation for Multi-access Edge Computing (2023)
  Zhiyuan Wu et al.
  4.13
• Entropy-driven Fair and Effective Federated Learning (2023)
  Lin Wang et al.
  4.13
• Federated Learning for Heterogeneous Electronic Health Record Systems with Cost Effective Participant Selection (2024)
  Jiyoun Kim et al.
  4.13
• Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization (2024)
  Khiem Le et al.
  4.08
• Federated Learning in Practice: Reflections and Projections (2024)
  Katharine Daly et al.
  4.08
• Floe: Federated Specialization for Real-Time LLM-SLM Inference (2026)
  Chunlin Tian et al.
  4.04
• From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare (2024)
  Ming Li et al.
  4.03
• Towards Privacy-Preserving Mental Health Support with Large Language Models (2026)
  Dong Xue et al.
  3.98
• SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces (2026)
  Tianwang Jia et al.
  3.98
• Towards Federated RLHF with Aggregated Client Preference for LLMs (2024)
  Feijie Wu et al.
  3.92
• Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT (2025)
  Anas Ali et al.
  3.81
• Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare (2025)
  Natallia Kokash and Lei Wang and Thomas H. Gillespie and Adam Belloum and Paola Grosso and Sara Quinney and Lang Li and Bernard de Bono
  3.81
• DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation (2024)
  Jie Xu et al.
  3.80