Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Distributed training of foundation models, especially large language models (LLMs), is
communication-intensive and so has heavily relied on centralized data centers with fast …

In federated learning, communication cost is often a critical bottleneck to scale up distributed
optimization algorithms to collaboratively learn a model from millions of devices with …

Huge scale machine learning problems are nowadays tackled by distributed optimization
algorithms, ie algorithms that leverage the compute power of many devices for training. The …

Federated Learning is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …

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

Sign-based algorithms (eg signSGD) have been proposed as a biased gradient
compression technique to alleviate the communication bottleneck in training large neural …

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 …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …