A Framework Integrating Federated Learning and Fog Computing Based on Client Sampling and Dynamic Thresholding Techniques

A Framework Integrating Federated Learning and Fog Computing Based on Client Sampling and Dynamic Thresholding Techniques

The exponential growth in the number of Internet of Things (IoT) devices and the vast quantity of data they generate present a significant challenge to the efficacy of traditional centralized training models. Federated Learning (FL) is a machine learning framework that effectively addresses this iss...

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Bibliographic Details
Main Authors: Dang van Thang, Artem Volkov, Ammar Muthanna, Ibrahim A. Elgendy, Reem Alkanhel, Dushantha Nalin K. Jayakody, Andrey Koucheryavy
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Federated learning
fog computing
client sampling
dynamic thresholding
Online Access:https://ieeexplore.ieee.org/document/11007538/
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