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Federated Learning of Gboard Language Models with Differential Privacy

Federated Learning of Gboard Language Models with Differential Privacy

29 May 2023
Zheng Xu
Yanxiang Zhang
Galen Andrew
Christopher A. Choquette-Choo
Peter Kairouz
H. B. McMahan
Jesse Rosenstock
Yuanbo Zhang
    FedML
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Papers citing "Federated Learning of Gboard Language Models with Differential Privacy"

11 / 61 papers shown
Title
Information Flow Control in Machine Learning through Modular Model
  Architecture
Information Flow Control in Machine Learning through Modular Model Architecture
Trishita Tiwari
Suchin Gururangan
Chuan Guo
Weizhe Hua
Sanjay Kariyappa
Udit Gupta
Wenjie Xiong
Kiwan Maeng
Hsien-Hsin S. Lee
G. E. Suh
19
6
0
05 Jun 2023
Selective Pre-training for Private Fine-tuning
Selective Pre-training for Private Fine-tuning
Da Yu
Sivakanth Gopi
Janardhan Kulkarni
Zi-Han Lin
Saurabh Naik
Tomasz Religa
Jian Yin
Huishuai Zhang
22
19
0
23 May 2023
Can Public Large Language Models Help Private Cross-device Federated
  Learning?
Can Public Large Language Models Help Private Cross-device Federated Learning?
Boxin Wang
Yibo Zhang
Yuan Cao
Bo-wen Li
H. B. McMahan
Sewoong Oh
Zheng Xu
Manzil Zaheer
FedML
11
37
0
20 May 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential
  Privacy
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Natalia Ponomareva
Hussein Hazimeh
Alexey Kurakin
Zheng Xu
Carson E. Denison
H. B. McMahan
Sergei Vassilvitskii
Steve Chien
Abhradeep Thakurta
94
167
0
01 Mar 2023
On the Convergence of Federated Averaging with Cyclic Client
  Participation
On the Convergence of Federated Averaging with Cyclic Client Participation
Yae Jee Cho
Pranay Sharma
Gauri Joshi
Zheng Xu
Satyen Kale
Tong Zhang
FedML
31
27
0
06 Feb 2023
One-shot Empirical Privacy Estimation for Federated Learning
One-shot Empirical Privacy Estimation for Federated Learning
Galen Andrew
Peter Kairouz
Sewoong Oh
Alina Oprea
H. B. McMahan
Vinith M. Suriyakumar
FedML
19
32
0
06 Feb 2023
Differentially Private Natural Language Models: Recent Advances and
  Future Directions
Differentially Private Natural Language Models: Recent Advances and Future Directions
Lijie Hu
Ivan Habernal
Lei Shen
Di Wang
AAML
13
18
0
22 Jan 2023
PiPar: Pipeline Parallelism for Collaborative Machine Learning
PiPar: Pipeline Parallelism for Collaborative Machine Learning
Zihan Zhang
Philip Rodgers
Peter Kilpatrick
I. Spence
Blesson Varghese
FedML
21
3
0
01 Dec 2022
Differentially Private Fine-tuning of Language Models
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
346
0
13 Oct 2021
A Field Guide to Federated Optimization
A Field Guide to Federated Optimization
Jianyu Wang
Zachary B. Charles
Zheng Xu
Gauri Joshi
H. B. McMahan
...
Mi Zhang
Tong Zhang
Chunxiang Zheng
Chen Zhu
Wennan Zhu
FedML
173
411
0
14 Jul 2021
Practical and Private (Deep) Learning without Sampling or Shuffling
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
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