… resources optimization challenge. In this paper, we consider federated learning for cellular
… latency and effect of loss in federated learning model accuracy due to channel uncertainties…

… new resource allocation problem for privacypreserving EdgeIoT to balance the learning
accuracy … We propose a new federated learning-enabled twin-delayed deep deterministic policy …

… As the data privacy and security concerns increase, federated learning (FL) … learning is
based on single BS connectivity, which can be limited, calling for hierarchical federated learning (…

… Federated learning (FL) is an emerging paradigm that enables multiple devices to collaborate
in training machine learning (ML) models without having to share their possibly private …

… a new resource allocation framework, which we call resource … new resource rationing
framework for wireless federated learning. Resource rationing takes a holistic view of the resource …

Federated learning (FL) has been employed for numerous privacy-sensitive applications, …
In this article, we propose a resource allocation scheme for FL, namely RaFed. We formulate …

… design aspects for enabling federated learning at the network edge. We … federated learning
via a Stackelberg game to motivate the participation of the devices in the federated learning …

… In this paper, we have presented the DQL algorithm for the resource allocation in the
mobility-aware federated learning network. In particular, we first formulate the channel selection …

… federated learning. This paper brings a new longterm perspective to resource allocation in
WFLNs, realizing that learning … varying significance towards the final learning outcome. To this …

… of our considered blockchain-based federated learning (BCFL) and then formulate the
problem from the perspective of resource allocation and incentive mechanism design based on …