Knowledge and Model-Driven Deep Reinforcement Learning for Federated Edge Learning

Federated edge learning (FEL) integrates federated learning (FL) into edge computing systems to improve communication efficiency and data privacy. We investigate a practical FEL system, where the computing and communication resources are dynamic and heterogeneous among workers, and the local data ar...

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Bibliographic Details

Published in:	IEEE Global Communications Conference (Online) pp. 3926 - 3931
Main Authors:	Li, Yangchen, Zhao, Lingzhi, Yang, Feng
Format:	Conference Proceeding
Language:	English
Published:	IEEE 08.12.2024
Subjects:	Convergence Deep reinforcement learning Dynamic scheduling Edge computing Federated learning Heuristic algorithms optimization System dynamics Time factors Training Transforms
ISSN:	2576-6813
Online Access:	Get full text
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