Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

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 …

Machine learning (ML) is widely used to derive useful information from large-scale data
(such as user activities, pictures, and videos) generated at increasingly rapid rates, all over …

Deep learning models can take weeks to train on a single GPU-equipped machine,
necessitating scaling out DL training to a GPU-cluster. However, current distributed DL …

How can one build a distributed framework that allows efficient deployment of a wide
spectrum of modern advanced machine learning (ML) programs for industrial-scale …

The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-
intensive applications such as ETL, query processing, or machine learning (ML). Several …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

Federated learning (FL) is a new technology that has been a hot research topic. It enables
the training of an algorithm across multiple decentralized edge devices or servers holding …