Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …

The advent of the 6G era aims for ubiquitous connectivity, with the integration of non-
terrestrial networks (NTN) offering extensive coverage and enhanced capacity. As …

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 involves training statistical models over remote devices or siloed data
centers, such as mobile phones or hospitals, while kee** data localized. Training in …

Federated learning is a distributed framework according to which a model is trained over a
set of devices, while kee** data localized. This framework faces several systems-oriented …

The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …

In federated learning (FL), devices contribute to the global training by uploading their local
model updates via wireless channels. Due to limited computation and communication …

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

The metaverse is regarded as a new wave of technological transformation that provides a
virtual space for people to interact through digital avatars. To achieve immersive user …

We consider a scenario involving computations over a massive dataset stored distributedly
across multiple workers, which is at the core of distributed learning algorithms. We propose …