Secure Quantized Training for Deep Learning

1 July 2021

Papers citing "Secure Quantized Training for Deep Learning"

20 / 20 papers shown

Title
VIRGOS: Secure Graph Convolutional Network on Vertically Split Data from Sparse Matrix Decomposition Yu Zheng Qizhi Zhang Lichun Li Kai Zhou Shan Yin GNN 60 0 0 17 Feb 2025
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning Wenqiang Ruan Xin Lin Ruisheng Zhou Guopeng Lin Shui Yu Weili Han 45 0 0 16 Feb 2025
Privacy-Preserving Power Flow Analysis via Secure Multi-Party Computation Jonas von der Heyden Nils Schlüter P. Binfet Martin Asman Markus Zdrallek Tibor Jager M. S. Darup 61 1 0 21 Nov 2024
CryptoTrain: Fast Secure Training on Encrypted Dataset Jiaqi Xue Yancheng Zhang YanShan Wang Xueqiang Wang Hao Zheng Qian Lou 24 3 0 25 Sep 2024
UTrace: Poisoning Forensics for Private Collaborative Learning Evan Rose Hidde Lycklama Harsh Chaudhari Anwar Hithnawi Alina Oprea 45 1 0 23 Sep 2024
How to Privately Tune Hyperparameters in Federated Learning? Insights from a Benchmark Study Natalija Mitic Apostolos Pyrgelis Sinem Sav FedML 58 1 0 25 Feb 2024
Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning Hidde Lycklama Alexander Viand Nicolas Küchler Christian Knabenhans Anwar Hithnawi 59 6 0 24 Feb 2024
CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources Sikha Pentyala Mayana Pereira Martine De Cock 29 1 0 13 Feb 2024
Spin: An Efficient Secure Computation Framework with GPU Acceleration Wuxuan Jiang Xiangjun Song Shenbai Hong Haijun Zhang Wenxin Liu Bo Zhao Wei Xu Yi Li 23 1 0 04 Feb 2024
Practical, Private Assurance of the Value of Collaboration Hassan Jameel Asghar Zhigang Lu Zhongrui Zhao Dali Kaafar FedML 27 0 0 04 Oct 2023
Attesting Distributional Properties of Training Data for Machine Learning Vasisht Duddu Anudeep Das Nora Khayata Hossein Yalame T. Schneider Nirmal Asokan 48 5 0 18 Aug 2023
Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects Conrad Sanderson David M. Douglas Qinghua Lu 40 11 0 17 Apr 2023
WW-FL: Secure and Private Large-Scale Federated Learning F. Marx T. Schneider Ajith Suresh Tobias Wehrle Christian Weinert Hossein Yalame FedML 25 2 0 20 Feb 2023
Secure Multiparty Computation for Synthetic Data Generation from Distributed Data Mayana Pereira Sikha Pentyala A. Nascimento R. T. D. Sousa Martine De Cock 13 6 0 13 Oct 2022
Privacy-Preserving Federated Recurrent Neural Networks Sinem Sav Abdulrahman Diaa Apostolos Pyrgelis Jean-Philippe Bossuat Jean-Pierre Hubaux 12 7 0 28 Jul 2022
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference Maximilian Lam Michael Mitzenmacher Vijay Janapa Reddi Gu-Yeon Wei David Brooks 39 3 0 05 Mar 2022
CECILIA: Comprehensive Secure Machine Learning Framework Ali Burak Ünal Nícolas Pfeifer Mete Akgun 25 2 0 07 Feb 2022
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU Sijun Tan Brian Knott Yuan Tian David J. Wu BDL FedML 57 183 0 22 Apr 2021
secureTF: A Secure TensorFlow Framework D. Quoc Franz Gregor Sergei Arnautov Roland Kunkel Pramod Bhatotia Christof Fetzer 44 40 0 20 Jan 2021
CrypTFlow2: Practical 2-Party Secure Inference Deevashwer Rathee Mayank Rathee Nishant Kumar Nishanth Chandran Divya Gupta Aseem Rastogi Rahul Sharma 87 301 0 13 Oct 2020