Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning-Reference-Cited by-同舟云学术

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Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning

Published:2023 Issue: Volume: Page:1-1
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Kim Do-Yup¹^ORCID,Lee Da-Eun²,Kim Ji-Wan³,Lee Hyun-Suk³^ORCID

Affiliation:

1. Department of Information and Communication AI Engineering, Kyungnam University, Changwon-si, South Korea

2. Department of Aritificial Intelligence Application, Hanyang University, Seoul, South Korea

3. Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, South Korea

Funder

Institute for Information and Communications Technology Promotion

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/6702522/10295960.pdf?arnumber=10295960

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Latency Minimization of Multi-Uav Assisted Federated Learning in Fast Decision-Making Scenarios;2024

2. Dynamic Joint Scheduling of Anycast Transmission and Modulation in Hybrid Unicast-Multicast SWIPT-Based IoT Sensor Networks;IEEE Sensors Journal;2023-12-15

3. Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification;IEEE Access;2023

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