Federated Learning with Differential Privacy for End-to-End Speech Recognition

29 September 2023

Papers citing "Federated Learning with Differential Privacy for End-to-End Speech Recognition"

7 / 7 papers shown

Title
Training Large ASR Encoders with Differential Privacy Geeticka Chauhan Steve Chien Om Thakkar Abhradeep Thakurta Arun Narayanan 15 1 0 21 Sep 2024
Stabilizing Transformer Training by Preventing Attention Entropy Collapse Shuangfei Zhai Tatiana Likhomanenko Etai Littwin Dan Busbridge Jason Ramapuram Yizhe Zhang Jiatao Gu J. Susskind AAML 38 64 0 11 Mar 2023
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? Sheikh Shams Azam Seyyedali Hosseinalipour Qiang Qiu Christopher G. Brinton FedML 10 20 0 01 Feb 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 64 181 0 06 Dec 2021
Federated Learning in ASR: Not as Easy as You Think Wentao Yu J. Freiwald Soren Tewes F. Huennemeyer D. Kolossa FedML 24 17 0 30 Sep 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 144 348 0 25 Sep 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 178 154 0 26 Feb 2021