As our dependence on intelligent machines continues to grow, so does the demand for more
transparent and interpretable models. In addition, the ability to explain the model generally …

The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …

Large language models have led to state-of-the-art accuracies across several tasks.
However, training these models efficiently is challenging because: a) GPU memory capacity …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

Privacy protection has been an important concern with the great success of machine
learning. In this paper, it proposes a multi-party privacy preserving machine learning …

Collaborative machine learning and related techniques such as federated learning allow
multiple participants, each with his own training dataset, to build a joint model by training …

The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …

Large-scale distributed training requires significant communication bandwidth for gradient
exchange that limits the scalability of multi-node training, and requires expensive high …

Distributed training of massive machine learning models, in particular deep neural networks,
via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of …