Applicability of Deep Reinforcement Learning for Efficient Federated Learning in Massive IoT Communications

Applicability of Deep Reinforcement Learning for Efficient Federated Learning in Massive IoT Communications

To build intelligent model learning in conventional architecture, the local data are required to be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage of personalization, and insufficient use of network resources. To address these issues, federated learning (FL) is...

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Bibliographic Details
Main Authors: Prohim Tam, Riccardo Corrado, Chanthol Eang, Seokhoon Kim
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Applied Sciences
Subjects:
communication-efficient learning
deep reinforcement learning
federated learning
massive Internet of Things
policy optimization
self-organizing networks
Online Access:https://www.mdpi.com/2076-3417/13/5/3083

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https://www.mdpi.com/2076-3417/13/5/3083

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