… learning accuracy level, and thus (ii) between the Federated Learning time and UE energy
consumption… We fill this gap by formulating a Federated Learning over wireless network as an …

… In Section III, we detail the local computing and parameter update process to run FL in
wireless networks. In Section IV, we analyze the convergence rate of federated learning under …

… from other decentralized learning approaches. Building on this, we discuss several key
applications of the federated learning framework in fifth generation (5G) networks spanning from …

… wireless network in training a shared ML model without data exchanges, federated learning
… a number of existing works (eg, [3–6]) have studied its use for wireless network optimization. …

… to train a learning model locally. … learning frameworks is federated learning (FL) developed
in [5]. FL is a distributed machine learning method that enables users to collaboratively learn …

… resource allocation for federated learning (FL) over wireless communication networks is
investigated… Since FL involves an exchange of a learning model between users and the BS, both …

… machine learning technique called Federated Learning (FL), … We then employ FEDL in wireless
networks as a resource … Even though the wireless resource allocation problem of FEDL is …

… Since all of the FL model parameters are transmitted over wireless networks, we must
consider the effect of wireless factors such as resource blocks, user selection, dynamic wireless …

… 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 …

… communications, including basic principles of federated learning, efficient communications
for training a federated learning model, and federated learning for intelligent wireless …