Accurate Prediction of Network Distance via Federated Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Accurate Prediction of Network Distance via Federated Deep Reinforcement Learning

Published:2024-08 Issue:4 Volume:32 Page:3301-3314
ISSN:1063-6692
Container-title:IEEE/ACM Transactions on Networking
language:
Short-container-title:IEEE/ACM Trans. Networking

Author:

Huang Haojun¹^ORCID,Cai Yiming¹,Min Geyong²^ORCID,Wang Haozhe²,Liu Gaoyang³,Wu Dapeng Oliver⁴^ORCID

Affiliation:

1. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China

2. Department of Computer Science, University of Exeter, Exeter, U.K.

3. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

4. Department of Computer Science, City University of Hong Kong, Hong Kong, China

Funder

Natural Science Foundation of China

Hong Kong Research Grant Council-General Research Fund

European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/90/10640200/10489912.pdf?arnumber=10489912

Reference30 articles.

1. Detailed Modeling of Heterogeneous and Contention-Constrained Point-to-Point MPI Communication

2. SpaceRTC: Unleashing the Low-latency Potential of Mega-constellations for Real-Time Communications

3. CHAT: Accurate Network Latency Measurement for 5G E2E Networks

4. Predicting Internet network distance with coordinates-based approaches

5. DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction

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1. Energy-Efficient Resource Allocation for V2X Communications;IEEE Internet of Things Journal;2024-09-15

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