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...

詳細記述

書誌詳細

出版年:	Sensors
主要な著者:	Saroj Mali, Feng Zeng, Deepak Adhikari, Inam Ullah, Mahmoud Ahmad Al-Khasawneh, Osama Alfarraj, Fahad Alblehai
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2025-03-01
主題:	federated reinforcement learning federated learning reinforcement learning edge computing internet of things
オンライン･アクセス:	https://www.mdpi.com/1424-8220/25/7/2197