Showing 1–20 of 41 results
/ Date/ Name
Feb 25, 2022Towards an Accountable and Reproducible Federated Learning: A FactSheets ApproachSep 3, 2022Federated XGBoost on Sample-Wise Non-IID DataDec 11, 2020Adaptive Histogram-Based Gradient Boosted Trees for Federated LearningAug 10, 2021Privacy-Preserving Machine Learning: Methods, Challenges and DirectionsJun 17, 2024Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMsJul 17, 2024Turning Generative Models Degenerate: The Power of Data Poisoning AttacksDec 18, 2025In-Context Probing for Membership Inference in Fine-Tuned Language ModelsDec 4, 2020Mitigating Bias in Federated LearningFeb 16, 2022Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & AnalysisJul 3, 2018Adversarial Robustness Toolbox v1.0.0Jul 12, 2022Federated Unlearning: How to Efficiently Erase a Client in FL?Jul 15, 2022DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust SettingDec 15, 2021FLoRA: Single-shot Hyper-parameter Optimization for Federated LearningFeb 13, 2024Rethinking Machine Unlearning for Large Language ModelsJun 2, 2025Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-TuningNov 9, 2018Detecting Backdoor Attacks on Deep Neural Networks by Activation ClusteringDec 7, 2018A Hybrid Approach to Privacy-Preserving Federated LearningFeb 1, 2021Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated LearningMar 5, 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned DataOct 30, 2023Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection