{"total":30,"rows":[{"id":"2202.08827","title":"LAMP: Extracting Text from Gradients with Language Model Priors","created":"2022-02-17","cited_by_count":54,"area":null},{"id":"2206.07284","title":"A Survey on Gradient Inversion: Attacks, Defenses and Future Directions","created":"2022-06-15","cited_by_count":40,"area":null},{"id":"2206.04055","title":"Gradient Obfuscation Gives a False Sense of Security in Federated Learning","created":"2022-06-08","cited_by_count":39,"area":null},{"id":"2212.03714","title":"Reconstructing Training Data from Model Gradient, Provably","created":"2022-12-07","cited_by_count":27,"area":null},{"id":"2206.12395","title":"Data Leakage in Federated Averaging","created":"2022-06-24","cited_by_count":26,"area":null},{"id":"2306.14123","title":"Privacy and Fairness in Federated Learning: on the Perspective of Trade-off","created":"2023-06-25","cited_by_count":26,"area":null},{"id":"2207.07223","title":"Accelerated Federated Learning with Decoupled Adaptive Optimization","created":"2022-07-14","cited_by_count":23,"area":null},{"id":"2403.03945","title":"SPEAR:Exact Gradient Inversion of Batches in Federated Learning","created":"2024-03-06","cited_by_count":15,"area":null},{"id":"2210.01785","title":"TabLeak: Tabular Data Leakage in Federated Learning","created":"2022-10-04","cited_by_count":13,"area":null},{"id":"2306.00127","title":"Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning","created":"2023-05-31","cited_by_count":11,"area":null},{"id":"2306.03013","title":"Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning","created":"2023-06-05","cited_by_count":9,"area":null},{"id":"2309.13002","title":"Expressive variational quantum circuits provide inherent privacy in federated learning","created":"2023-09-22","cited_by_count":8,"area":null},{"id":"2309.13016","title":"Understanding Deep Gradient Leakage via Inversion Influence Functions","created":"2023-09-22","cited_by_count":6,"area":null},{"id":"2402.09478","title":"Data Reconstruction Attacks and Defenses: A Systematic Evaluation","created":"2024-02-13","cited_by_count":6,"area":null},{"id":"2402.03124","title":"Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks","created":"2024-02-05","cited_by_count":4,"area":null},{"id":"2311.10500","title":"From Principle to Practice: Vertical Data Minimization for Machine Learning","created":"2023-11-17","cited_by_count":3,"area":null},{"id":"2401.11748","title":"GI-PIP: Do We Require Impractical Auxiliary Dataset for Gradient Inversion Attacks?","created":"2024-01-22","cited_by_count":3,"area":null},{"id":"2406.00999","title":"Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients","created":"2024-06-03","cited_by_count":3,"area":null},{"id":"2502.19070","title":"A Sample-Level Evaluation and Generative Framework for Model Inversion Attacks","created":"2025-02-26","cited_by_count":3,"area":null},{"id":"2206.00769","title":"Defense Against Gradient Leakage Attacks via Learning to Obscure Data","created":"2022-06-01","cited_by_count":2,"area":null},{"id":"2208.04767","title":"Combining Stochastic Defenses to Resist Gradient Inversion: An Ablation Study","created":"2022-08-09","cited_by_count":2,"area":null},{"id":"2212.02042","title":"Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning","created":"2022-12-05","cited_by_count":2,"area":null},{"id":"2309.04515","title":"Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks","created":"2023-09-08","cited_by_count":2,"area":null},{"id":"2510.03319","title":"SVDefense: Effective Defense against Gradient Inversion Attacks via Singular Value Decomposition","created":"2025-10-01","cited_by_count":2,"area":null},{"id":"2406.01112","title":"BACON: Bayesian Optimal Condensation Framework for Dataset Distillation","created":"2024-06-03","cited_by_count":1,"area":null},{"id":"2506.12031","title":"Improving Generalization in Heterogeneous Federated Continual Learning via Spatio-Temporal Gradient Matching with Prototypical Coreset","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2408.12119","title":"Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective","created":"2024-08-22","cited_by_count":0,"area":null},{"id":"2508.04155","title":"Evaluating Selective Encryption Against Gradient Inversion Attacks","created":"2025-08-06","cited_by_count":0,"area":null},{"id":"2509.09742","title":"Images in Motion?: A First Look into Video Leakage in Collaborative Deep Learning","created":"2025-09-11","cited_by_count":0,"area":null},{"id":"2602.11633","title":"TIP: Resisting Gradient Inversion via Targeted Interpretable Perturbation in Federated Learning","created":"2026-02-12","cited_by_count":0,"area":null}],"next_cursor":null}