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

In federated learning, all networked clients contribute to the model training cooperatively.
However, with model sizes increasing, even sharing the trained partial models often leads to …

Decentralized training of deep learning models is a key element for enabling data privacy
and on-device learning over networks. In realistic learning scenarios, the presence of …

Federated Learning marks a turning point in the implementation of decentralized machine
learning (especially deep learning) for wireless devices by protecting users' privacy and …

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as
smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Federated learning (FL) enables distribution of machine learning workloads from the cloud
to resource-limited edge devices. Unfortunately, current deep networks remain not only too …

Wider coverage and a better solution to a latency reduction in 5G necessitate its
combination with multi-access edge computing technology. Decentralized deep learning …

Federated learning enables resource-constrained edge compute devices, such as mobile
phones and IoT devices, to learn a shared model for prediction, while keeping the training …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …