In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …

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 Machine Learning, the emergence of the right to be forgotten gave birth to a paradigm
named machine unlearning, which enables data holders to proactively erase their data from …

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 collaborative machine learning (CML), multiple agents pool their resources (eg, data)
together for a common learning task. In realistic CML settings where the agents are self …

Federated learning allows edge devices to collaboratively train a global model by
synchronizing their local updates without sharing private data. Yet, with limited network …

The emerging Federated Learning (FL) enables IoT devices to collaboratively learn a
shared model based on their local datasets. However, due to end devices' heterogeneity, it …

Federated learning is an emerging concept that trains the machine learning models with the
local distributed data sets, without sending the raw data to the data center. But, in the …

Distributed synchronous stochastic gradient descent has been widely used to train deep
neural networks on computer clusters. With the increase of computational power, network …

Data-parallel training is widely used for scaling DNN training over large datasets, using the
parameter server or all-reduce architecture. Communication scheduling has been promising …