Showing 1–16 of 16 results
/ Date/ Name
Dec 23, 2021Sparsified Secure Aggregation for Privacy-Preserving Federated LearningDec 15, 2016Lossy Coding of Correlated Sources over a Multiple Access Channel: Necessary Conditions and Separation ResultsNov 3, 2020A Scalable Approach for Privacy-Preserving Collaborative Machine LearningFeb 10, 2021Energy-Harvesting Distributed Machine LearningFeb 2, 2019CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine LearningFeb 11, 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated LearningFeb 22, 2021Sustainable Federated LearningJun 4, 2025Gradient Inversion Attacks on Parameter-Efficient Fine-TuningJun 6, 2025A Certified Unlearning Approach without Access to Source DataFeb 13, 2024FLASH: Federated Learning Across Simultaneous HeterogeneitiesSep 29, 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated LearningOct 5, 2021Secure Aggregation for Buffered Asynchronous Federated LearningAug 20, 2025Towards Source-Free Machine UnlearningJul 21, 2020Byzantine-Resilient Secure Federated LearningJun 7, 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated LearningJan 6, 2024Plug-and-Play Transformer Modules for Test-Time Adaptation