As our dependence on intelligent machines continues to grow, so does the demand for more
transparent and interpretable models. In addition, the ability to explain the model generally …

Deep learning has disrupted nearly every field of research, including those of direct
importance to drug discovery, such as medicinal chemistry and pharmacology. This …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Training deep neural networks requires gradient estimation from data batches to update
parameters. Gradients per parameter are averaged over a set of data and this has been …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …

In federated learning, heterogeneity in the clients' local datasets and computation speeds
results in large variations in the number of local updates performed by each client in each …

Recent studies show that private training data can be leaked through the gradients sharing
mechanism deployed in distributed machine learning systems, such as federated learning …

We address the problem of Federated Learning (FL) where users are distributed and
partitioned into clusters. This setup captures settings where different groups of users have …

This paper presents the design, implementation, and evaluation of the PyTorch distributed
data parallel module. PyTorch is a widely-adopted scientific computing package used in …