Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …

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 …

We propose a novel federated learning method for distributively training neural network
models, where the server orchestrates cooperation between a subset of randomly chosen …

Federated learning (FL) has been a popular method to achieve distributed machine learning
among numerous devices without sharing their data to a cloud server. FL aims to learn a …

Due to the inherent model uncertainty, learning to infer Bayesian posterior from a few-shot
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …

Recent advancements in deep neural networks (DNN) enabled various mobile deep
learning applications. However, it is technically challenging to locally train a DNN model due …

With more regulations tackling the protection of users' privacy-sensitive data in recent years,
access to such data has become increasingly restricted. A new decentralized training …

Abstract Stochastic Gradient Descent (SGD) is arguably the most important single algorithm
in modern machine learning. Although SGD with unbiased gradient estimators has been …

We consider a federated learning framework in which a parameter server (PS) trains a
global model by using n clients without actually storing the client data centrally at a cloud …