gradient attack

IBAS:Imperceptible Backdoor Attacks in Split Learning with Limited Information AAAI 2025 Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data NeurIPS 2024 DiffHammer: Rethinking the Robustness of Diffusion-Based Adversarial Purification NeurIPS 2024 Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning ICLR 2024 Safely Learning with Private Data: A Federated Learning Framework for Large Language Model EMNLP 2024 TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases NeurIPS 2024 Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models ICLR 2023 Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks ICML 2023 An Equivalence Between Data Poisoning and Byzantine Gradient Attacks ICML 2022 FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning IJCAI 2022 Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification ICML 2022 Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios IJCAI 2022 Recovering Private Text in Federated Learning of Language Models NeurIPS 2022 Revealing and Protecting Labels in Distributed Training NeurIPS 2021 R-GAP: Recursive Gradient Attack on Privacy ICLR 2021 TAG: Gradient Attack on Transformer-based Language Models EMNLP 2021 On Breaking Deep Generative Model-based Defenses and Beyond ICML 2020 TextHide: Tackling Data Privacy in Language Understanding Tasks EMNLP 2020 A Little Is Enough: Circumventing Defenses For Distributed Learning NeurIPS 2019