Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

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 deep learning, different kinds of deep networks typically need different optimizers, which
have to be chosen after multiple trials, making the training process inefficient. To relieve this …

Federated learning and analytics are a distributed approach for collaboratively learning
models (or statistics) from decentralized data, motivated by and designed for privacy …

Most popular optimizers for deep learning can be broadly categorized as adaptive methods
(eg~ Adam) and accelerated schemes (eg~ stochastic gradient descent (SGD) with …

Abstract Deep Neural Networks (DNNs) are widely regarded as the most effective learning
tool for dealing with large datasets, and they have been successfully used in thousands of …

Deep learning has recently unleashed the ability for Machine learning (ML) to make
unparalleled strides. It did so by confronting and successfully addressing, at least to a …

This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …

Abstract Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of
the loss landscape and generalization, has demonstrated a significant performance boost …

Federated Learning (FL) is a novel approach enabling several clients holding sensitive data
to collaboratively train machine learning models, without centralizing data. The cross-silo FL …