Recent advances in Information Retrieval utilise energy-intensive hardware to produce state-
of-the-art results. In areas of research highly related to Information Retrieval, such as Natural …

Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while kee** all the raw training …

Federated Learning (FL) is a collaborative training paradigm whereby a global Machine
Learning (ML) model is trained using typically private and distributed data sources without …

Modern deep learning applications require increasingly more compute to train state-of-the-
art models. To address this demand, large corporations and institutions use dedicated High …

Federated learning is one of the most appealing alternatives to the standard centralized
learning paradigm, allowing a heterogeneous set of devices to train a machine learning …

Deep learning contributes to reaching higher levels of artificial intelligence. Due to its
pervasive adoption, however, growing concerns on the environmental impact of this …

Federated Learning (FL) introduces a novel methodology with the potential to achieve
enhanced privacy and security assurances compared to existing methods. This is achieved …

Energy efficiency is essential for federated learning (FL) over mobile devices and its
potential prosperous applications. Different from existing communication efficient FL …

The development of AI applications, especially in large-scale wireless networks, is growing
exponentially, alongside the size and complexity of the architectures used. Particularly …

Energy consumption and carbon emissions are expected to be crucial factors for Internet of
Things (IoT) applications. Both the scale and the geo-distribution keep increasing, while …