Federated learning with differential privacy and an untrusted aggregator

17 December 2023

Papers citing "Federated learning with differential privacy and an untrusted aggregator"

10 / 10 papers shown

Title
Hercules: Boosting the Performance of Privacy-preserving Federated Learning Guowen Xu Xingshuo Han Shengmin Xu Tianwei Zhang Hongwei Li Xinyi Huang R. Deng FedML 14 16 0 11 Jul 2022
FedLess: Secure and Scalable Federated Learning Using Serverless Computing Andreas Grafberger Mohak Chadha Anshul Jindal Jianfeng Gu Michael Gerndt 36 49 0 05 Nov 2021
SecFL: Confidential Federated Learning using TEEs D. Quoc Christof Fetzer FedML 16 16 0 03 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 144 348 0 25 Sep 2021
Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection Shulai Zhang Zirui Li Quan Chen Wenli Zheng Jingwen Leng M. Guo FedML 54 32 0 08 Sep 2021
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU Sijun Tan Brian Knott Yuan Tian David J. Wu BDL FedML 55 182 0 22 Apr 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
Secure Weighted Aggregation for Federated Learning Jiale Guo Ziyao Liu K. Lam Jun Zhao Yiqiang Chen C. Xing 79 13 0 17 Oct 2020
FedML: A Research Library and Benchmark for Federated Machine Learning Chaoyang He Songze Li Jinhyun So Xiao Zeng Mi Zhang ... Yang Liu Ramesh Raskar Qiang Yang M. Annavaram Salman Avestimehr FedML 162 564 0 27 Jul 2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 948 20,549 0 17 Apr 2017