… features of federated ML, which differentiate it from other decentralized learning approaches.
Building on this, we discuss several key applications of the federated learning framework in …

… learning in resource and privacy-constrained systems such as the Internet of Things, federated
learning … enabling edge devices to train a shared learning model while promoting privacy. …

… In this section, we will go through a brief overview of the theoretical aspects of both
centralized learning, distributed learning and federated learning. 4.1 Centralized learning …

… for federated learning (FL) over wireless communication … learning model between users
and the BS, both computation and communication latencies are determined by the learning …

… communications of federated learning. We have also discussed some wireless application
examples, which reflect the power of federated learning for future wireless systems. However, …

… federated learning (FL) algorithms over a realistic wireless network is studied. In the considered
model, wireless … over wireless links, the quality of training is affected by wireless factors …

The conventional federated learning (FL) framework usually assumes synchronous reception
and fusion of all the local models at the central aggregator and synchronous updating and …

… study federated machine learning at the wireless network edge, where limited power wireless
… We consider a bandwidth-limited fading multiple access channel (MAC) from the wireless …

… a new wireless communication … learning and provide potential federated learning applications
for 6G. We then describe key technical challenges, the corresponding federated learning …

… is federated learning (FL), which enables the devices to train a common machine learning
… In particular, the essential requirements for applying FL to wireless communications are first …