One of the fundamental issues for Federated Learning (FL) is data heterogeneity, which
causes accuracy degradation, slow convergence, and the communication bottleneck issue …

Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing
the sample complexity of reinforcement learning tasks by exploiting information from …

The growing literature of Federated Learning (FL) has recently inspired Federated
Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better …

Personalized federated learning, as a variant of federated learning, trains customized
models for clients using their heterogeneously distributed data. However, it is still …

In this paper, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In particular, in the considered model, wireless users perform an …

Federated learning (FL) is promising in enabling large-scale model training by massive
devices without exposing their local datasets. However, due to limited wireless resources …

Federated learning (FL) is a decentralized machine learning architecture, which leverages a
large number of remote devices to learn a joint model with distributed training data …

Federated learning is a communication-efficient training process that alternate between
local training at the edge devices and averaging of the updated local model at the center …

In this paper, the convergence time of federated learning (FL), when deployed over a
realistic wireless network, is studied. In particular, with the considered model, wireless users …

Most existing wireless federated learning (FL) studies focused on homogeneous model
settings where devices train identical local models. In this setting, the devices with poor …