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 …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Nowadays, the training data and neural network models are getting increasingly large. The
training time of deep learning will become unbearably long on a single machine. To reduce …

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

The increasing size of machine learning models, especially deep neural network models,
can improve the model generalization capability. However, large models require more …

Distributed synchronous stochastic gradient descent (SGD) algorithms are widely used in
large-scale deep learning applications, while it is known that the communication bottleneck …

Federated Learning (FL) is an emerging decentralized learning framework through which
multiple clients can collaboratively train a learning model. However, a major obstacle that …

Federated Learning (FL) systems orchestrate the cooperative training of a shared Machine
Learning (ML) model across connected devices. Recently, decentralized FL architectures …

Federated Learning has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …

Deep learning's widespread adoption in various fields has made distributed training across
multiple computing nodes essential. However, frequent communication between nodes can …