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 …

Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …

Along with the rapid developments in communication technologies and the surge in the use
of mobile devices, a brand-new computation paradigm, edge computing, is surging in …

With the breakthroughs in deep learning, the recent years have witnessed a booming of
artificial intelligence (AI) applications and services, spanning from personal assistant to …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

In recent years, along with the blooming of Machine Learning (ML)-based applications and
services, ensuring data privacy and security have become a critical obligation. ML-based …

Federated learning (FL) is emerging as a new paradigm to train machine learning (ML)
models in distributed systems. Rather than sharing and disclosing the training data set with …

We present ByteScheduler, a generic communication scheduler for distributed DNN training
acceleration. ByteScheduler is based on our principled analysis that partitioning and …

Recently, federated learning (FL) techniques have enabled multiple users to train machine
learning models collaboratively without data sharing. However, existing FL algorithms suffer …