3 papers
cs.CR2025
Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization
Shuangqing Xu, Yifeng Zheng, Zhongyun Hua
Federated learning (FL) enables multiple clients to jointly train a model by sharing only gradient updates for aggregation instead of raw data. Due to the transmission of very high…
cs.CR2024
Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy
Shuangqing Xu, Yifeng Zheng, Zhongyun Hua
Federated learning (FL) has rapidly become a compelling paradigm that enables multiple clients to jointly train a model by sharing only gradient updates for aggregation, without re…
cs.CR2023
Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables
Yifeng Zheng, Shuangqing Xu, Songlei Wang +2
Vertical federated learning (VFL) has recently emerged as an appealing distributed paradigm empowering multi-party collaboration for training high-quality models over vertically pa…