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Found 7 papers, 1 papers with code
Analytic Personalized Federated Meta-Learning
To overcome the first challenge, we propose an AFL framework, namely FedACnnL, in which a layer-wise DNN collaborative training method is designed by modeling the training of each layer as a distributed LS problem.
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
In this paper, we propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme to achieve heterogeneity-robustness in the presence of quantized transmission and heterogeneous local updates among active edge devices.
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
In the traditional definition of AoI, it is assumed that the status information can be actively sampled and directly used.
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence
With the obtained FL hyperparameters and resource allocation, we design a MAML-based FL algorithm, called Automated Federated Learning (AutoFL), that is able to conduct fast adaptation and convergence.
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks.
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks
Experimental results verify the effectiveness of PerFedS2 in saving training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.
Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning
This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users.
