Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design

8 November 2022

Papers citing "Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design"

9 / 9 papers shown

Title
PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy Linh Tran Timothy Castiglia Stacy Patterson Ana Milanova FedML 40 0 0 23 Jan 2025
Exactly Minimax-Optimal Locally Differentially Private Sampling Hyun-Young Park Shahab Asoodeh Si-Hyeon Lee 24 1 0 30 Oct 2024
Privacy-Aware Randomized Quantization via Linear Programming Zhongteng Cai Xueru Zhang Mohammad Mahdi Khalili 33 2 0 01 Jun 2024
Universal Exact Compression of Differentially Private Mechanisms Yanxiao Liu Wei-Ning Chen Ayfer Özgür Cheuk Ting Li 33 2 0 28 May 2024
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data Richeng Jin Yujie Gu Kai Yue Xiaofan He Zhaoyang Zhang Huaiyu Dai FedML 20 0 0 16 Feb 2024
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri L. V. D. van der Maaten 103 53 0 28 Jan 2022
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 55 157 0 14 Feb 2021
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 157 760 0 28 Sep 2019
Optimal Geo-Indistinguishable Mechanisms for Location Privacy N. E. Bordenabe K. Chatzikokolakis C. Palamidessi 34 275 0 20 Feb 2014