4 citations · 4 across the 3 of their papers we have counts for
6 papers
3D Spectrum Awareness for Radio Dynamic Zones Using Kriging and Matrix Completion
Mushfiqur Rahman, Sung Joon Maeng, Ismail Guvenc +1
Radio Dynamic Zones (RDZs) are geographically defined areas specifically allocated for testing new wireless technologies. It is essential to safeguard the regular spectrum users ou…
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…
UAV-Assisted Coverage Hole Detection Using Reinforcement Learning in Urban Cellular Networks
Mushfiqur Rahman, Ismail Guvenc, David Ramirez +1
Deployment of cellular networks in urban areas requires addressing various challenges. For example, high-rise buildings with varying geometrical shapes and heights contribute to si…
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…