103 citations · 187 across the 16 of their papers we have counts for
6 papers · 1 filter
Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction
Guangji Bai, Ziyang Yu, Zheng Chai +2
Despite the recent success of Graph Neural Networks (GNNs), it remains challenging to train GNNs on large-scale graphs due to neighbor explosions. As a remedy, distributed computin…
Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization
Zheng Chai, Guangji Bai, Liang Zhao +1
Despite the recent success of Graph Neural Networks, it remains challenging to train a GNN on large graphs with millions of nodes and billions of edges, which are prevalent in many…
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
Ahmad Faraz Khan, Yuze Li, Xinran Wang +5
Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT ap…
Community-based Layerwise Distributed Training of Graph Convolutional Networks
Hongyi Li, Junxiang Wang, Yongchao Wang +2
The Graph Convolutional Network (GCN) has been successfully applied to many graph-based applications. Training a large-scale GCN model, however, is still challenging: Due to the no…
Asynchronous Federated Learning for Sensor Data with Concept Drift
Yujing Chen, Zheng Chai, Yue Cheng +1
Federated learning (FL) involves multiple distributed devices jointly training a shared model without any of the participants having to reveal their local data to a centralized ser…
TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad +7
Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that ex…