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research-article

Hierarchical Adaptive Collaborative Learning: A Distributed Learning Framework for Customized Cloud Services in 6G Mobile Systems

Authors:

Haibing GuanAuthors Info & Claims

IEEE Network, Volume 37, Issue 2

Pages 44 - 53

https://doi.org/10.1109/MNET.001.2200382

Published: 01 March 2023 Publication History

Abstract

The Fifth Generation (5G) mobile systems support many kinds of intelligent cloud services. The upcoming Sixth Generation (6G) mobile systems aim to provide customized cloud services since the users have different capabilities and needs. However, as an essential scenario in 6G mobile systems, collaborative AI failed to deliver customized cloud services to the users because of system heterogeneity. Due to the lack of efficient resource coordination and service orchestration strategy, collaborative learning suffers from high latency and low resource utilization. This article proposes Hierarchical Adaptive Collaborative Learning, a distributed learning framework for customized cloud services in 6G mobile systems, to improve the training efficiency and resource utilization by dynamically adjusting the collaborative training process according to the service and resources. We introduce the critical scenarios and challenges of distributed learning. Then we present our framework to handle heterogeneous cloud services and resources. Next, we illustrate various model training and collaborating techniques to improve training efficiency. Finally, we provide a case study to show the priority of the modules in our framework and provide future research directions on collaborative learning for customized cloud services in 6G mobile systems.

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Cited By

Wang RZhang JZhang QZhang BGu ZAttarpour AJi YTornatore MSekar VYu MSeneviratne AVeitch D(2024)Poster: Flexible Scheduling of Network and Computing Resources for Distributed AI TasksProceedings of the ACM SIGCOMM 2024 Conference: Posters and Demos10.1145/3672202.3673744(60-62)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3672202.3673744
Fan SWang CRuan XShi HMa RGuan H(2024)FedREAS: A Robust Efficient Aggregation and Selection Framework for Federated LearningACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3670689Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3670689

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cover image IEEE Network: The Magazine of Global Internetworking

IEEE Network: The Magazine of Global Internetworking Volume 37, Issue 2

March/April 2023

299 pages

ISSN:0890-8044

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Copyright © 2023.

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IEEE Press

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Published: 01 March 2023

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Cited By

Wang RZhang JZhang QZhang BGu ZAttarpour AJi YTornatore MSekar VYu MSeneviratne AVeitch D(2024)Poster: Flexible Scheduling of Network and Computing Resources for Distributed AI TasksProceedings of the ACM SIGCOMM 2024 Conference: Posters and Demos10.1145/3672202.3673744(60-62)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3672202.3673744
Fan SWang CRuan XShi HMa RGuan H(2024)FedREAS: A Robust Efficient Aggregation and Selection Framework for Federated LearningACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3670689Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3670689

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