Exploring Federated Unlearning: Review, Comparison, and Insights

30 October 2023

Papers citing "Exploring Federated Unlearning: Review, Comparison, and Insights"

9 / 9 papers shown

Title
MUNBa: Machine Unlearning via Nash Bargaining Jing Wu Mehrtash Harandi MU 123 5 0 23 Nov 2024
Threats, Attacks, and Defenses in Machine Unlearning: A Survey Ziyao Liu Huanyi Ye Chen Chen Yongsen Zheng K. Lam AAML MU 95 31 0 20 Mar 2024
VeriFi: Towards Verifiable Federated Unlearning Xiangshan Gao Xingjun Ma Jingyi Wang Youcheng Sun Bo Li S. Ji Peng Cheng Jiming Chen MU 94 48 0 25 May 2022
Federated Unlearning with Knowledge Distillation Chen Wu Sencun Zhu P. Mitra MU 49 109 0 24 Jan 2022
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 489 1,923 0 14 Dec 2020
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain Tianyu Gu Brendan Dolan-Gavitt S. Garg SILM 120 1,772 0 22 Aug 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 208 2,894 0 14 Mar 2017
Embedding Watermarks into Deep Neural Networks Yusuke Uchida Yuki Nagai S. Sakazawa Shiníchi Satoh 122 609 0 15 Jan 2017
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 255 4,135 0 18 Oct 2016