The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

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 …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Distributed training of foundation models, especially large language models (LLMs), is
communication-intensive and so has heavily relied on centralized data centers with fast …

Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

Federated learning is a distributed framework according to which a model is trained over a
set of devices, while kee** data localized. This framework faces several systems-oriented …

DNN training is extremely time-consuming, necessitating efficient multi-accelerator
parallelization. Current approaches to parallelizing training primarily use intra-batch …