Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Recently, federated learning (FL) techniques have enabled multiple users to train machine
learning models collaboratively without data sharing. However, existing FL algorithms suffer …

In recent years, privacy concerns have become a serious issue for companies wishing to
protect economic models and comply with end-user expectations. In the same vein, some …

In public clouds, users must manually select a datacenter region to upload their ML training
data and launch ML training workloads in the same region to ensure data and computation …

With the growing model size of deep neural networks (DNN), deep learning training is
increasingly relying on handcrafted search spaces to find efficient parallelization execution …

Training large deep learning models at scale is very challenging. This paper proposes
Chimera, a novel pipeline parallelism scheme which combines bidirectional pipelines for …

The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …