29 citations · 74 across the 7 of their papers we have counts for
17 papers
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
Chaoyang He, Emir Ceyani, Keshav Balasubramanian +2
Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-str…
Byzantine-Robust and Privacy-Preserving Framework for FedML
Hanieh Hashemi, Yongqin Wang, Chuan Guo +1
Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others…
Privacy and Integrity Preserving Training Using Trusted Hardware
Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train with private data while exploiting acceler…
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani +11
Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a ma…
Distributed Training of Graph Convolutional Networks using Subgraph Approximation
Alexandra Angerd, Keshav Balasubramanian, Murali Annavaram
Modern machine learning techniques are successfully being adapted to data modeled as graphs. However, many real-world graphs are typically very large and do not fit in memory, ofte…
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
Chaoyang He, Murali Annavaram, Salman Avestimehr
Scaling up the convolutional neural network (CNN) size (e.g., width, depth, etc.) is known to effectively improve model accuracy. However, the large model size impedes training on…