3 papers
cs.LG2025
Adaptive Graph Coarsening for Efficient GNN Training
Rostyslav Olshevskyi, Madeline Navarro, Santiago Segarra
We propose an adaptive graph coarsening method to jointly learn graph neural network (GNN) parameters and merge nodes via K-means clustering during training. As real-world graphs g…
cs.LG2024
Fully Distributed Online Training of Graph Neural Networks in Networked Systems
Rostyslav Olshevskyi, Zhongyuan Zhao, Kevin Chan +3
Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power gr…
cs.CV2024
Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection
Ahmad Khalil, Tizian Dege, Pegah Golchin +3
In the pursuit of refining precise perception models for fully autonomous driving, continual online model training becomes essential. Federated Learning (FL) within vehicular netwo…