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 …

Federated learning and analytics are a distributed approach for collaboratively learning
models (or statistics) from decentralized data, motivated by and designed for privacy …

Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Adversarial training, which minimizes the maximal risk for label-preserving input
perturbations, has proved to be effective for improving the generalization of language …

We study federated machine learning (ML) at the wireless edge, where power-and
bandwidth-limited wireless devices with local datasets carry out distributed stochastic …

We study the asynchronous stochastic gradient descent algorithm, for distributed training
over $ n $ workers that might be heterogeneous. In this algorithm, workers compute …

Federated learning (FL) has attracted increasing attention as a promising approach to
driving a vast number of end devices with artificial intelligence. However, it is very …

Communication-efficient SGD algorithms, which allow nodes to perform local updates and
periodically synchronize local models, are highly effective in improving the speed and …

When training machine learning models using stochastic gradient descent (SGD) with a
large number of nodes or massive edge devices, the communication cost of synchronizing …