Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks

8 September 2023

Papers citing "Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks"

5 / 5 papers shown

Title
Analyzing Inference Privacy Risks Through Gradients in Machine Learning Zhuohang Li Andrew Lowy Jing Liu T. Koike-Akino K. Parsons Bradley Malin Ye Wang FedML 38 1 0 29 Aug 2024
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
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification Jiancheng Yang Rui Shi D. Wei Zequan Liu Lin Zhao B. Ke Hanspeter Pfister Bingbing Ni VLM 183 648 0 27 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 159 350 0 25 Sep 2021
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 147 178 0 28 Jul 2020