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 …

Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …

Abstract In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train
Machine Learning (ML) models across the participants of a federation while preserving data …

Most of the current literature focused on centralized learning is centered around the
celebrated average-consensus paradigm and less attention is devoted to scenarios where …

With the rapid development of wireless communication, achieving the neXt generation Ultra-
Reliable and Low-Latency Communications (xURLLC) in 6G mobile communication …

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) represents a promising distributed machine learning paradigm that
allows smart devices to collaboratively train a shared model via providing local data sets …

Emerging applications in multiagent environments such as internet-of-things, networked
sensing, autonomous systems, and federated learning, call for decentralized algorithms for …

Personalized federated learning (PFL) aims to produce the greatest personalized model for
each client to face an insurmountable problem--data heterogeneity in real FL systems …