This chapter presents a survey that focuses on the implementation of federated learning (FL)
techniques in sixth generation (6G) networks' physical layer (PHY) to meet the increasing …

With the rapid advancements in edge computing and artificial intelligence, federated
learning (FL) has gained momentum as a promis⁃ ing approach to collaborative data …

The 5 G networks are effectively deployed worldwide, and academia and industries have
begun looking at 6 G network communication technology for consumer electronics …

New technological advancements in wireless networks have enlarged the number of
connected devices. The unprecedented surge of data volume in wireless systems …

To alleviate the shortage of computing power faced by clients in training deep neural
networks (DNNs) using federated learning (FL), we leverage the edge computing and split …

Optimal algorithm design for federated learning (FL) remains an open problem. This paper
explores the full potential of FL in practical edge computing systems where workers may …

The rapid progress and deployment beyond 5G and moving towards a 6G network demand
the stipulation for network intelligence by applying advanced ML-driven approaches. The …

Due to the increasing demand from mobile devices for the real-time response of cloud
computing services, federated edge learning (FEL) emerges as a new computing paradigm …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Federated learning (FL) is an effective paradigm for training a machine-learning model
based on data distributed at a large quantity of users in Internet of Things (IoT) without …