Federated Ensemble Model-Based Reinforcement Learning in Edge Computing

Jin Wang; Jia Hu; Jed Mills; Geyong Min; Ming Xia; Nektarios Georgalas

doi:10.1109/tpds.2023.3264480

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Federated Ensemble Model-Based Reinforcement Learning in Edge Computing

Jin Wang Jia Hu Jed Mills Geyong Min Ming Xia Nektarios Georgalas

Year: 2023 Journal: IEEE Transactions on Parallel and Distributed Systems Vol: 34 (6)Pages: 1848-1859 Publisher: Institute of Electrical and Electronics Engineers

DOI: 10.1109/tpds.2023.3264480

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Abstract

This is the author accepted manuscript. The final version is available from the IEEE via the DOI in this record

Keywords:

Computer science Reinforcement learning Benchmark (surveying) Ensemble learning Enhanced Data Rates for GSM Evolution Artificial intelligence Machine learning Sample complexity Edge computing Sample (material)

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Privacy-Preserving Technologies in Data

Physical Sciences → Computer Science → Artificial Intelligence

Mobile Crowdsensing and Crowdsourcing

Physical Sciences → Computer Science → Computer Science Applications

IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

Federated Ensemble Model-Based Reinforcement Learning in Edge Computing

Abstract

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