Privacy-Aware Compression for Federated Data Analysis

15 March 2022

Papers citing "Privacy-Aware Compression for Federated Data Analysis"

8 / 8 papers shown

Title
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Eric Aubinais Philippe Formont Pablo Piantanida Elisabeth Gassiat 50 0 0 10 Feb 2025
Universal Exact Compression of Differentially Private Mechanisms Yanxiao Liu Wei-Ning Chen Ayfer Özgür Cheuk Ting Li 42 2 0 28 May 2024
Communication Efficient Private Federated Learning Using Dithering Burak Hasircioglu Deniz Gunduz FedML 45 7 0 14 Sep 2023
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks Natalie Lang Nir Shlezinger Rafael G. L. DÓliveira S. E. Rouayheb FedML 75 4 0 01 Aug 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$ -Differential Privacy Richeng Jin Z. Su C. Zhong Zhaoyang Zhang Tony Q.S. Quek H. Dai FedML 29 2 0 19 Feb 2023
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 33 7 0 08 Nov 2022
Joint Privacy Enhancement and Quantization in Federated Learning Natalie Lang Elad Sofer Tomer Shaked Nir Shlezinger FedML 37 46 0 23 Aug 2022
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