… federated learning (FL) in a classic wireless network, where learning clients share a common
wireless link to a coordinating server to perform federated model training using their local …

… Abstract—This paper studies a federated learning (FL) system, where multiple FL … wireless
network and share common wireless resources. It fills the void of wireless resource allocation …

… allocation method. In this paper, we propose a joint device scheduling and bandwidth
allocation framework for implementing FL in wireless networks. Specifically, deep reinforcement …

… effect of wireless communications. However, the computation delay of the local FL training
model was not taken into account. An energy-efficient strategy for bandwidth allocation and …

… 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 …

… Devetsikiotis, “Jointly optimizing client selection and resource management in wireless
federated learning for Internet of Things,” IEEE Internet Things J., early access, Aug. 10, 2021, doi…

… Specifically, we formulate a joint bandwidth allocation and scheduling problem to
maximize the accuracy of the trained model, and further decouple the problem into two sub-problems, …

… 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 …

… allocation problem for FEDL over wireless networks to capture the trade-off between the wall
clock training … -problems relate to UE resource allocation over wireless networks, which are …

… and bandwidth allocation to minimize a weighted combination of the … training delay and
training loss. Liu et al. proposed network-assisted HFL in [10], where they optimized wireless …