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 …

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 …

Federated learning (FL) allows model training from local data collected by edge/mobile
devices while preserving data privacy, which has wide applicability to image and vision …

The ever-growing artificial intelligence (AI) applications have greatly reshaped our world in
many areas, eg, smart home, computer vision, natural language processing, etc. Behind …

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

Federated learning (FL) is an emerging technique for training machine learning models
using geographically dispersed data collected by local entities. It includes local computation …

In recent years, wireless federated learning (FL) has been proposed to support the mobile
intelligent applications over the wireless network, which protects the data privacy and …

Communication overhead is one of the major obstacles to train large deep learning models
at scale. Gradient sparsification is a promising technique to reduce the communication …

Abstract We propose Compressed Vertical Federated Learning (C-VFL) for communication-
efficient training on vertically partitioned data. In C-VFL, a server and multiple parties …

Distributed training techniques have been widely deployed in large-scale deep models
training on dense-GPU clusters. However, on public cloud clusters, due to the moderate …