31 citations · 76 across the 16 of their papers we have counts for
5 papers · 1 filter
Extracting Spatiotemporal Data from Gradients with Large Language Models
Lele Zheng, Yang Cao, Renhe Jiang +4
Recent works show that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primar…
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
Fumiyuki Kato, Li Xiong, Shun Takagi +2
Differentially Private Federated Learning (DP-FL) has garnered attention as a collaborative machine learning approach that ensures formal privacy. Most DP-FL approaches ensure DP a…
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification
Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
Combining Federated Learning (FL) with a Trusted Execution Environment (TEE) is a promising approach for realizing privacy-preserving FL, which has garnered significant academic at…
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release
Donghao Li, Yang Cao, Yuan Yao
Privacy-preserving data release algorithms have gained increasing attention for their ability to protect user privacy while enabling downstream machine learning tasks. However, the…
FL-Market: Trading Private Models in Federated Learning
Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa +2
The difficulty in acquiring a sufficient amount of training data is a major bottleneck for machine learning (ML) based data analytics. Recently, commoditizing ML models has been pr…