Randomized Quantization is All You Need for Differential Privacy in Federated Learning

20 June 2023

Papers citing "Randomized Quantization is All You Need for Differential Privacy in Federated Learning"

8 / 8 papers shown

Title
Prompt Inversion Attack against Collaborative Inference of Large Language Models Wenjie Qu Yuguang Zhou Yongji Wu Tingsong Xiao Binhang Yuan Y. Li Jiaheng Zhang 71 0 0 12 Mar 2025
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Eric Aubinais Philippe Formont Pablo Piantanida Elisabeth Gassiat 38 0 0 10 Feb 2025
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
Scalable Differential Privacy Mechanisms for Real-Time Machine Learning Applications Jessica Smith David Williams Emily Brown 26 0 0 16 Sep 2024
Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point Bokun Wang Axel Berg D. A. E. Acar Chuteng Zhou FedML MQ 39 0 0 02 Jul 2024
Layered Randomized Quantization for Communication-Efficient and Privacy-Preserving Distributed Learning Guangfeng Yan Tan Li Tian-Shing Lan Kui Wu Linqi Song 19 6 0 12 Dec 2023
FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings Jean Ogier du Terrail Quentin Klopfenstein Honghao Li Imke Mayer Nicolas Loiseau Mohammad Hallal Félix Balazard M. Andreux 16 2 0 28 Nov 2023
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 162 760 0 28 Sep 2019