Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework

16 December 2022

Papers citing "Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework"

6 / 6 papers shown

Title
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
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing? Keshigeyan Chandrasegaran Ngoc-Trung Tran Yunqing Zhao Ngai-man Cheung 86 41 0 29 Jun 2022
Memorization in NLP Fine-tuning Methods Fatemehsadat Mireshghallah Archit Uniyal Tianhao Wang David E. Evans Taylor Berg-Kirkpatrick AAML 61 39 0 25 May 2022
On a Utilitarian Approach to Privacy Preserving Text Generation Zekun Xu Abhinav Aggarwal Oluwaseyi Feyisetan Nathanael Teissier 34 24 0 23 Apr 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
Teaching Machines to Read and Comprehend Karl Moritz Hermann Tomás Kociský Edward Grefenstette L. Espeholt W. Kay Mustafa Suleyman Phil Blunsom 181 3,510 0 10 Jun 2015