{"total":440,"rows":[{"id":"2104.07586","title":"See through Gradients: Image Batch Recovery via GradInversion","created":"2021-04-15","cited_by_count":326,"area":null},{"id":"2101.04535","title":"Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning","created":"2021-01-11","cited_by_count":199,"area":null},{"id":"2102.13451","title":"FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout","created":"2021-02-26","cited_by_count":192,"area":null},{"id":"2310.06816","title":"Text Embeddings Reveal (Almost) As Much As Text","created":"2023-10-10","cited_by_count":192,"area":null},{"id":"2106.06639","title":"Federated Learning with Buffered Asynchronous Aggregation","created":"2021-06-11","cited_by_count":190,"area":null},{"id":"2206.07758","title":"Reconstructing Training Data from Trained Neural Networks","created":"2022-06-15","cited_by_count":133,"area":null},{"id":"2104.02151","title":"Distributed Learning in Wireless Networks: Recent Progress and Future Challenges","created":"2021-04-05","cited_by_count":129,"area":null},{"id":"2102.06387","title":"The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation","created":"2021-02-12","cited_by_count":128,"area":null},{"id":"2112.02918","title":"When the Curious Abandon Honesty: Federated Learning Is Not Private","created":"2021-12-06","cited_by_count":127,"area":null},{"id":"2301.07014","title":"Dataset Distillation: A Comprehensive Review","created":"2023-01-17","cited_by_count":127,"area":null},{"id":"2206.00240","title":"Privacy for Free: How does Dataset Condensation Help Privacy?","created":"2022-06-01","cited_by_count":120,"area":null},{"id":"2302.04062","title":"Machine Learning for Synthetic Data Generation: A Review","created":"2023-02-08","cited_by_count":120,"area":null},{"id":"2110.13057","title":"Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models","created":"2021-10-25","cited_by_count":108,"area":null},{"id":"2110.14962","title":"Gradient Inversion with Generative Image Prior","created":"2021-10-28","cited_by_count":101,"area":null},{"id":"2204.00032","title":"Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets","created":"2022-03-31","cited_by_count":101,"area":null},{"id":"2104.14380","title":"PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments","created":"2021-04-29","cited_by_count":93,"area":null},{"id":"2302.08888","title":"Multimodal Federated Learning via Contrastive Representation Ensemble","created":"2023-02-17","cited_by_count":86,"area":null},{"id":"2102.05429","title":"Node-Level Membership Inference Attacks Against Graph Neural Networks","created":"2021-02-10","cited_by_count":84,"area":null},{"id":"2201.08135","title":"Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges","created":"2022-01-20","cited_by_count":84,"area":null},{"id":"2103.06191","title":"A Study of Face Obfuscation in ImageNet","created":"2021-03-10","cited_by_count":83,"area":null},{"id":"2101.06570","title":"Membership Inference Attack on Graph Neural Networks","created":"2021-01-17","cited_by_count":81,"area":null},{"id":"2109.12273","title":"FedProc: Prototypical Contrastive Federated Learning on Non-IID data","created":"2021-09-25","cited_by_count":81,"area":null},{"id":"2203.15696","title":"Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage","created":"2022-03-29","cited_by_count":72,"area":null},{"id":"2205.08514","title":"Recovering Private Text in Federated Learning of Language Models","created":"2022-05-17","cited_by_count":70,"area":null},{"id":"2111.07380","title":"Eluding Secure Aggregation in Federated Learning via Model Inconsistency","created":"2021-11-14","cited_by_count":67,"area":null},{"id":"2104.14362","title":"From Distributed Machine Learning to Federated Learning: A Survey","created":"2021-04-29","cited_by_count":66,"area":null},{"id":"2102.01854","title":"Provably Secure Federated Learning against Malicious Clients","created":"2021-02-03","cited_by_count":63,"area":null},{"id":"2101.11073","title":"Property Inference From Poisoning","created":"2021-01-26","cited_by_count":60,"area":null},{"id":"2111.11343","title":"Decentralized Federated Learning through Proxy Model Sharing","created":"2021-11-22","cited_by_count":57,"area":null},{"id":"2101.07511","title":"Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge","created":"2021-01-19","cited_by_count":56,"area":null},{"id":"2203.01925","title":"Label-Only Model Inversion Attacks via Boundary Repulsion","created":"2022-03-03","cited_by_count":56,"area":null},{"id":"2201.12675","title":"Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models","created":"2022-01-29","cited_by_count":53,"area":null},{"id":"2203.11894","title":"GradViT: Gradient Inversion of Vision Transformers","created":"2022-03-22","cited_by_count":53,"area":null},{"id":"2204.05011","title":"FederatedScope: A Flexible Federated Learning Platform for Heterogeneity","created":"2022-04-11","cited_by_count":53,"area":null},{"id":"2306.09486","title":"FedMultimodal: A Benchmark For Multimodal Federated Learning","created":"2023-06-15","cited_by_count":51,"area":null},{"id":"2305.16157","title":"Training Data Extraction From Pre-trained Language Models: A Survey","created":"2023-05-25","cited_by_count":45,"area":null},{"id":"2308.04699","title":"GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization","created":"2023-08-09","cited_by_count":45,"area":null},{"id":"2107.01614","title":"Survey: Leakage and Privacy at Inference Time","created":"2021-07-04","cited_by_count":44,"area":null},{"id":"2106.06089","title":"Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix","created":"2021-06-10","cited_by_count":43,"area":null},{"id":"2408.12787","title":"LLM-PBE: Assessing Data Privacy in Large Language Models","created":"2024-08-23","cited_by_count":41,"area":null},{"id":"2107.01154","title":"Gradient-Leakage Resilient Federated Learning","created":"2021-07-02","cited_by_count":40,"area":null},{"id":"2110.09074","title":"Towards General Deep Leakage in Federated Learning","created":"2021-10-18","cited_by_count":40,"area":null},{"id":"2402.12959","title":"Prompt Stealing Attacks Against Large Language Models","created":"2024-02-20","cited_by_count":40,"area":null},{"id":"2203.03761","title":"The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning","created":"2022-03-07","cited_by_count":39,"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":"2310.18362","title":"SoK: Memorization in General-Purpose Large Language Models","created":"2023-10-24","cited_by_count":37,"area":null},{"id":"2105.00529","title":"GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning","created":"2021-05-02","cited_by_count":36,"area":null},{"id":"2303.12233","title":"LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation","created":"2023-03-21","cited_by_count":36,"area":null},{"id":"2109.05659","title":"Source Inference Attacks in Federated Learning","created":"2021-09-13","cited_by_count":34,"area":null},{"id":"2111.04706","title":"Bayesian Framework for Gradient Leakage","created":"2021-11-08","cited_by_count":34,"area":null},{"id":"2303.11242","title":"Make Landscape Flatter in Differentially Private Federated Learning","created":"2023-03-20","cited_by_count":33,"area":null},{"id":"2111.11066","title":"FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks","created":"2021-11-22","cited_by_count":32,"area":null},{"id":"2305.14888","title":"Privacy Implications of Retrieval-Based Language Models","created":"2023-05-24","cited_by_count":32,"area":null},{"id":"2201.02873","title":"LoMar: A Local Defense Against Poisoning Attack on Federated Learning","created":"2022-01-08","cited_by_count":31,"area":null},{"id":"2208.12348","title":"SNAP: Efficient Extraction of Private Properties with Poisoning","created":"2022-08-25","cited_by_count":31,"area":null},{"id":"2106.04405","title":"Federated Neural Collaborative Filtering","created":"2021-06-02","cited_by_count":30,"area":null},{"id":"2310.01424","title":"Identifying and Mitigating Privacy Risks Stemming from Language Models: A Survey","created":"2023-09-27","cited_by_count":30,"area":null},{"id":"2111.01108","title":"Resource-Efficient Federated Learning","created":"2021-11-01","cited_by_count":26,"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":"2107.06018","title":"This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces","created":"2021-07-13","cited_by_count":25,"area":null},{"id":"2210.08371","title":"Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability","created":"2022-10-15","cited_by_count":25,"area":null},{"id":"2401.17319","title":"Decentralized Federated Learning: A Survey on Security and Privacy","created":"2024-01-25","cited_by_count":25,"area":null},{"id":"2106.09680","title":"Accuracy, Interpretability, and Differential Privacy via Explainable Boosting","created":"2021-06-17","cited_by_count":24,"area":null},{"id":"2302.04787","title":"On the Privacy-Robustness-Utility Trilemma in Distributed Learning","created":"2023-02-09","cited_by_count":24,"area":null},{"id":"2302.10637","title":"A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy","created":"2023-02-21","cited_by_count":24,"area":null},{"id":"2205.08443","title":"On the (In)security of Peer-to-Peer Decentralized Machine Learning","created":"2022-05-17","cited_by_count":23,"area":null},{"id":"2406.05392","title":"Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey","created":"2024-06-08","cited_by_count":23,"area":null},{"id":"2103.01953","title":"Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation","created":"2021-03-02","cited_by_count":22,"area":null},{"id":"2207.08869","title":"FLAIR: Federated Learning Annotated Image Repository","created":"2022-07-18","cited_by_count":22,"area":null},{"id":"2310.00339","title":"FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation","created":"2023-09-30","cited_by_count":22,"area":null},{"id":"2110.14019","title":"Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection","created":"2021-10-26","cited_by_count":21,"area":null},{"id":"2209.01100","title":"Group Property Inference Attacks Against Graph Neural Networks","created":"2022-09-02","cited_by_count":21,"area":null},{"id":"2306.04959","title":"FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs","created":"2023-06-08","cited_by_count":21,"area":null},{"id":"2307.00974","title":"Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions","created":"2023-07-03","cited_by_count":21,"area":null},{"id":"2307.10655","title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","created":"2023-07-20","cited_by_count":21,"area":null},{"id":"2104.13061","title":"Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity","created":"2021-04-27","cited_by_count":19,"area":null},{"id":"2202.01971","title":"Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization","created":"2022-02-04","cited_by_count":19,"area":null},{"id":"2103.01548","title":"PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization","created":"2021-03-02","cited_by_count":18,"area":null},{"id":"2105.12049","title":"Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs","created":"2021-05-25","cited_by_count":18,"area":null},{"id":"2108.06910","title":"A Novel Attribute Reconstruction Attack in Federated Learning","created":"2021-08-16","cited_by_count":18,"area":null},{"id":"2112.13178","title":"Gradient Leakage Attack Resilient Deep Learning","created":"2021-12-25","cited_by_count":18,"area":null},{"id":"2405.01031","title":"The Privacy Power of Correlated Noise in Decentralized Learning","created":"2024-05-02","cited_by_count":18,"area":null},{"id":"2102.01375","title":"Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges","created":"2021-02-02","cited_by_count":17,"area":null},{"id":"2111.00556","title":"Revealing and Protecting Labels in Distributed Training","created":"2021-10-31","cited_by_count":17,"area":null},{"id":"2303.08355","title":"Efficient and Secure Federated Learning for Financial Applications","created":"2023-03-15","cited_by_count":17,"area":null},{"id":"2308.09883","title":"Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning","created":"2023-08-19","cited_by_count":17,"area":null},{"id":"2312.10108","title":"Privacy-Aware Document Visual Question Answering","created":"2023-12-15","cited_by_count":17,"area":null},{"id":"2111.06290","title":"Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System","created":"2021-11-11","cited_by_count":16,"area":null},{"id":"2207.08988","title":"Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices","created":"2022-07-18","cited_by_count":16,"area":null},{"id":"2301.04017","title":"Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation","created":"2023-01-09","cited_by_count":16,"area":null},{"id":"2302.01677","title":"Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks","created":"2023-02-03","cited_by_count":16,"area":null},{"id":"2304.02782","title":"FACE-AUDITOR: Data Auditing in Facial Recognition Systems","created":"2023-04-05","cited_by_count":16,"area":null},{"id":"2409.09727","title":"From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare","created":"2024-09-15","cited_by_count":16,"area":null},{"id":"2108.03980","title":"Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness","created":"2021-07-30","cited_by_count":15,"area":null},{"id":"2109.00675","title":"FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning","created":"2021-09-02","cited_by_count":15,"area":null},{"id":"2109.12062","title":"SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning","created":"2021-09-24","cited_by_count":15,"area":null},{"id":"2203.05222","title":"Similarity-based Label Inference Attack against Training and Inference of Split Learning","created":"2022-03-10","cited_by_count":15,"area":null},{"id":"2204.13784","title":"AGIC: Approximate Gradient Inversion Attack on Federated Learning","created":"2022-04-28","cited_by_count":15,"area":null},{"id":"2208.09967","title":"Inferring Sensitive Attributes from Model Explanations","created":"2022-08-21","cited_by_count":15,"area":null}],"next_cursor":"100"}