Federated Reinforcement Learning-Based Dynamic Resource Allocation and Task Scheduling in Edge for IoT Applications

Federated Reinforcement Learning-Based Dynamic Resource Allocation and Task Scheduling in Edge for IoT Applications

Using Google cluster traces, the research presents a task offloading algorithm and a hybrid forecasting model that unites Bidirectional Long Short-Term Memory (BiLSTM) with Gated Recurrent Unit (GRU) layers along an attention mechanism. This model predicts resource usage for flexible task scheduling...

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Bibliographic Details
Main Authors:	Saroj Mali, Feng Zeng, Deepak Adhikari, Inam Ullah, Mahmoud Ahmad Al-Khasawneh, Osama Alfarraj, Fahad Alblehai
Format:	Article
Language:	English
Published:	MDPI AG 2025-03-01
Series:	Sensors
Subjects:	federated reinforcement learning federated learning reinforcement learning edge computing internet of things
Online Access:	https://www.mdpi.com/1424-8220/25/7/2197
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