The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …

We consider a many-to-one wireless architecture for federated learning at the network edge,
where multiple edge devices collaboratively train a model using local data. The unreliable …

Decentralized federated learning, inherited from decentralized learning, enables the edge
devices to collaborate on model training in a peer-to-peer manner without the assistance of …

Decentralised learning is attracting more and more interest because it embodies the
principles of data minimisation and focused data collection, while favouring the transparency …

Nowadays, devices are equipped with advanced sensors with higher processing and
computing capabilities. Besides, widespread Internet availability enables communication …

To leverage rich data distributed at the network edge, a new machine-learning paradigm,
called edge learning, has emerged where learning algorithms are deployed at the edge for …

We study federated learning (FL), where power-limited wireless devices utilize their local
datasets to collaboratively train a global model with the help of a remote parameter server …

Federated learning encapsulates distributed learning strategies that are managed by a
central unit. Since it relies on using a selected number of agents at each iteration, and since …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …