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Make Landscape Flatter in Differentially Private Federated Learning

Make Landscape Flatter in Differentially Private Federated Learning

20 March 2023
Yi Shi
Yingqi Liu
Kang Wei
Li Shen
Xueqian Wang
Dacheng Tao
    FedML
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Papers citing "Make Landscape Flatter in Differentially Private Federated Learning"

12 / 12 papers shown
Title
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates
Chuanyin Wang
Yifei Zhang
Neng Gao
Qiang Luo
FedML
71
0
0
12 Mar 2025
Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization
Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization
Dun Zeng
Zheshun Wu
Shiyu Liu
Yu Pan
Xiaoying Tang
Zenglin Xu
MLT
FedML
89
1
0
25 Nov 2024
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance
Haichao Sha
Ruixuan Liu
Yi-xiao Liu
Hong Chen
52
1
0
06 Dec 2023
Heterogeneous Federated Learning: State-of-the-art and Research
  Challenges
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Mang Ye
Xiuwen Fang
Bo Du
PongChi Yuen
Dacheng Tao
FedML
AAML
39
244
0
20 Jul 2023
Practical Sharpness-Aware Minimization Cannot Converge All the Way to
  Optima
Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima
Dongkuk Si
Chulhee Yun
28
15
0
16 Jun 2023
Dynamic Regularized Sharpness Aware Minimization in Federated Learning:
  Approaching Global Consistency and Smooth Landscape
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
Yan Sun
Li Shen
Shi-Yong Chen
Liang Ding
Dacheng Tao
FedML
34
33
0
19 May 2023
Towards the Flatter Landscape and Better Generalization in Federated
  Learning under Client-level Differential Privacy
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
Yi Shi
Kang Wei
Li Shen
Yingqi Liu
Xueqian Wang
Bo Yuan
Dacheng Tao
FedML
38
2
0
01 May 2023
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning
  Rate and Momentum for Training Deep Neural Networks
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks
Hao Sun
Li Shen
Qihuang Zhong
Liang Ding
Shi-Yong Chen
Jingwei Sun
Jing Li
Guangzhong Sun
Dacheng Tao
49
31
0
01 Mar 2023
FedSpeed: Larger Local Interval, Less Communication Round, and Higher
  Generalization Accuracy
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy
Yan Sun
Li Shen
Tiansheng Huang
Liang Ding
Dacheng Tao
FedML
36
51
0
21 Feb 2023
Unraveling the Connections between Privacy and Certified Robustness in
  Federated Learning Against Poisoning Attacks
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
Chulin Xie
Yunhui Long
Pin-Yu Chen
Qinbin Li
Arash Nourian
Sanmi Koyejo
Bo Li
FedML
43
13
0
08 Sep 2022
Efficient Sharpness-aware Minimization for Improved Training of Neural
  Networks
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks
Jiawei Du
Hanshu Yan
Jiashi Feng
Qiufeng Wang
Liangli Zhen
Rick Siow Mong Goh
Vincent Y. F. Tan
AAML
113
132
0
07 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch
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
168
350
0
25 Sep 2021
1