HCP: Heterogeneous Computing Platform for Federated Learning Based Collaborative Content Caching Towards 6G Networks

A heterogeneous computing architecture is essential to facilitate intelligent network traffic control for a joint computation, communication, and collaborative caching optimization in 6G networks to provide stringent Quality of Experience (QoE) guarantees. In this paper, we consider a 6G integrated...

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Vydáno v:IEEE transactions on emerging topics in computing Ročník 10; číslo 1; s. 112 - 123
Hlavní autoři: Md. Fadlullah, Zubair, Kato, Nei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
6G mobile communication
Algorithms
Base stations
Caching
Cloud computing
Collaboration
collaborative caching
Communications traffic
Computational modeling
Computer architecture
Data models
edge computing
Federated learning
Heterogeneous networks
heterogeneouscomputing platform (HCP)
Intelligent networks
Machine learning
Numerical analysis
Optimization
Privacy
Radio equipment
Traffic control
unmanned Aerial Vehicle (UAV)
Unmanned aerial vehicles
ISSN:2168-6750, 2168-6750
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Shrnutí:A heterogeneous computing architecture is essential to facilitate intelligent network traffic control for a joint computation, communication, and collaborative caching optimization in 6G networks to provide stringent Quality of Experience (QoE) guarantees. In this paper, we consider a 6G integrated aerial-terrestrial network model where Unmanned Aerial Vehicles (UAVs) and terrestrial Remote Radio Heads (RRHs) jointly serve as heterogeneous Base Stations (hgNBs) of a Cloud Radio Access Network (HCRAN) serving different mobile user (UE) types. We propose a distributed heterogeneous computing platform (HCP) across the UAVs and terrestrial Base Stations (BSs) by utilizing their caching and cooperative communication capabilities. In order to preserve the privacy of the content of the UEs, we propose a 2-stage federated learning algorithm among the UEs, UAVs/BSs, and HCP to collaboratively predict the content caching placement by jointly considering traffic distribution, UE mobility and localized content popularity. An asynchronous weight updating method is adopted to avoid redundant learning transfer in the federated learning. Once the global model is learnt by the HCP, it transfers the learned model to the UEs to facilitate the much desired edge intelligence in the considered 6G tiny cell. The effectiveness of the proposal is evaluated by extensive numerical analysis.
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ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2020.2986238