Motivated by the advancing computational capacity of wireless end-user equipment (UE), as
well as the increasing concerns about sharing private data, a new machine learning (ML) …

Statistical inference and machine-learning algorithms have traditionally been developed for
data available at a single location. Unlike this centralized setting, modern data sets are …

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

Machine learning has begun to play a central role in many applications. A multitude of these
applications typically also involve datasets that are distributed across multiple computing …

We present two novel federated learning (FL) schemes that mitigate the effect of straggling
devices by introducing redundancy on the devices' data across the network. Compared to …

The simultaneous development of deep learning techniques and Internet of Things
(IoT)/Cyber-physical Systems (CPS) technologies has afforded untold possibilities for …

While machine learning is going through an era of celebrated success, concerns have been
raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent …

Machine learning (ML) over distributed multi-party data is required for a variety of domains.
Existing approaches, such as federated learning, collect the outputs computed by a group of …

The widespread use of mobile devices, as well as the increasing popularity of mobile
services has raised serious cybersecurity challenges. In the last years, the number of …

We consider the distributed statistical learning problem over decentralized systems that are
prone to adversarial attacks. This setup arises in many practical applications, including …