Recovering Private Text in Federated Learning of Language Models

Recovering Private Text in Federated Learning of Language Models

17 May 2022

Papers citing "Recovering Private Text in Federated Learning of Language Models"

17 / 17 papers shown

Title
Selective Attention Federated Learning: Improving Privacy and Efficiency for Clinical Text Classification Yue Li L. Zhang 34 0 0 16 Apr 2025
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions Hao Du Shang Liu Lele Zheng Yang Cao Atsuyoshi Nakamura Lei Chen AAML 114 3 0 21 Dec 2024
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security Youyang Qu Ming Liu Tianqing Zhu Longxiang Gao Shui Yu Wanlei Zhou MU FedML 65 2 0 14 Jun 2024
DAGER: Exact Gradient Inversion for Large Language Models Ivo Petrov Dimitar I. Dimitrov Maximilian Baader Mark Niklas Muller Martin Vechev FedML 60 3 0 24 May 2024
AdapterSwap: Continuous Training of LLMs with Data Removal and Access-Control Guarantees William Fleshman Aleem Khan Marc Marone Benjamin Van Durme CLL KELM 55 3 0 12 Apr 2024
BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models Haoxiang Luo Jian Luo Athanasios V. Vasilakos 31 9 0 10 Oct 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 43 23 0 20 Jul 2023
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Ashwinee Panda Xinyu Tang Saeed Mahloujifar Vikash Sehwag Prateek Mittal 43 11 0 08 Dec 2022
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning Ruihan Wu Xiangyu Chen Chuan Guo Kilian Q. Weinberger FedML 12 26 0 19 Oct 2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning Samuel Maddock Alexandre Sablayrolles Pierre Stock FedML 14 22 0 06 Oct 2022
Dropout is NOT All You Need to Prevent Gradient Leakage Daniel Scheliga Patrick Mäder M. Seeland FedML 27 12 0 12 Aug 2022
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models Liam H. Fowl Jonas Geiping Steven Reich Yuxin Wen Wojtek Czaja Micah Goldblum Tom Goldstein FedML 73 56 0 29 Jan 2022
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 69 181 0 06 Dec 2021
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 347 0 13 Oct 2021
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning Shaoxiong Ji Yue Tan Teemu Saravirta Zhiqin Yang Yixin Liu Lauri Vasankari Shirui Pan Guodong Long A. Walid FedML 37 76 0 25 Feb 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 290 1,815 0 14 Dec 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 355 0 07 Dec 2020