As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …

Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

Pushing artificial intelligence (AI) from central cloud to network edge has reached board
consensus in both industry and academia for materializing the vision of artificial intelligence …

It is well understood that client-master communication can be a primary bottleneck in
Federated Learning. In this work, we address this issue with a novel client subsampling …

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

The Internet of Things (IoT) has paved the way to a highly connected society where all things
are interconnected and exchanging information has become more accessible through the …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

The development of applications based on artificial intelligence and implemented over
wireless networks is increasingly rapidly and is expected to grow dramatically in the future …

Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …

Federated Learning (FL) allows clients to form a consortium to train a global model under
the orchestration of a central server while keeping data on the local client without sharing it …