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 …
B Xu, W Xia, W Wen, P Liu, H Zhao… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
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 …
X Li, Z Qu, B Tang, Z Lu - IEEE Transactions on Cybernetics, 2023 - ieeexplore.ieee.org
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 …
F Ang, L Chen, N Zhao, Y Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
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 …
Z Chen, W Yi, H Shin… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
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 …