The explosive growth of smart devices (eg, mobile phones, vehicles, drones) with sensing,
communication, and computation capabilities gives rise to an unprecedented amount of …

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

In this paper, a vision for beyond-5G systems is proposed where automation and
intelligence in cloud-native infrastructures are in focus. Exploiting the convergence of cloud …

Federated learning (FL) is a distributed machine learning technology for next-generation AI
systems that allows a number of workers, ie, edge devices, collaboratively learn a shared …

Federated Learning (FL) has emerged as a promising framework for distributed training of AI-
based services, applications, and network procedures in 6G. One of the major challenges …

The continuous convergence of machine learning algorithms, 5G and beyond (5G+)
wireless communications, and artificial intelligence (AI) hardware implementation hastens …

In this chapter, we provide a vision for edge intelligence as a key building block of 6G
wireless systems. As we evolve towards a new breed of wireless services, enabled by an …

Recently, Federated Learning (FL) has been applied in various research domains specially
because of its privacy preserving and decentralized approach of model training. However …

As the beyond 5G era approaches, the number of private 5G networks that can effectively
support various vertical services continues to increase. Naturally, federated learning (FL) is …

Abstract 6G is expected to support the unprecedented Internet of everything scenarios with
extremely diverse and challenging requirements. To fulfill such diverse requirements …