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

1 May 2023

Li Shen

Papers citing "Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy"

5 / 5 papers shown

Title
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training Yi Shi Yingqi Liu Yan Sun Zihao Lin Li Shen Xueqian Wang Dacheng Tao FedML 45 10 0 24 May 2023
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 Yan Sun Li Shen Tiansheng Huang Liang Ding Dacheng Tao FedML 36 51 0 21 Feb 2023
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 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