1 citations · 1 across the 5 of their papers we have counts for
5 papers
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning (FL) enables decentralized machine learning without sharing raw data, allowing multiple clients to collaboratively learn a global model. However, studies reveal…
Federated Learning Nodes Can Reconstruct Peers' Image Data
Ethan Wilson, Kai Yue, Chau-Wai Wong +1
Federated learning (FL) is a privacy-preserving machine learning framework that enables multiple nodes to train models on their local data and periodically average weight updates t…
NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel
Gabriel Thompson, Kai Yue, Chau-Wai Wong +1
Decentralized federated learning (DFL) is a collaborative machine learning framework for training a model across participants without a central server or raw data exchange. DFL fac…
Advancing Hybrid Defense for Byzantine Attacks in Federated Learning
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning (FL) enables multiple clients to collaboratively train a global model without sharing their local data. Recent studies have highlighted the vulnerability of FL t…
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
Richeng Jin, Yujie Gu, Kai Yue +3
Distributed training of deep neural networks faces three critical challenges: privacy preservation, communication efficiency, and robustness to fault and adversarial behaviors. Alt…