13 citations · 16 across the 4 of their papers we have counts for
4 papers
Gradient Obfuscation Gives a False Sense of Security in Federated Learning
Kai Yue, Richeng Jin, Chau-Wai Wong +2
Federated learning has been proposed as a privacy-preserving machine learning framework that enables multiple clients to collaborate without sharing raw data. However, client priva…
Neural Tangent Kernel Empowered Federated Learning
Kai Yue, Richeng Jin, Ryan Pilgrim +3
Federated learning (FL) is a privacy-preserving paradigm where multiple participants jointly solve a machine learning problem without sharing raw data. Unlike traditional distribut…
Federated Learning via Plurality Vote
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning allows collaborative workers to solve a machine learning problem while preserving data privacy. Recent studies have tackled various challenges in federated learn…
Communication-Efficient Federated Learning via Predictive Coding
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless m…