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 …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …

Privacy protection has been an important concern with the great success of machine
learning. In this paper, it proposes a multi-party privacy preserving machine learning …

Federated Learning is the current state-of-the-art in supporting secure multi-party machine
learning (ML): data is maintained on the owner's device and the updates to the model are …

Distributed training of deep learning models on large-scale training data is typically
conducted with asynchronous stochastic optimization to maximize the rate of updates, at the …

Deep learning models can take weeks to train on a single GPU-equipped machine,
necessitating scaling out DL training to a GPU-cluster. However, current distributed DL …

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 …

The emerging concern about data privacy and security has motivated the proposal of
federated learning, which allows nodes to only synchronize the locally-trained models …

Efficiently scheduling deep learning jobs on large-scale GPU clusters is crucial for job
performance, system throughput, and hardware utilization. It is getting ever more …