Guest Editorial: Special Issue on Resource-Efficient Collaborative Deep Learning Over B5G/6G Networks

被引：0

作者：

Brik, Bouziane ^{[1
]}

Bennis, Mehdi ^{[2
]}

Wang, Xianbin ^{[3
]}

Guizani, Mohsen ^{[4
]}

机构：

[1] Sharjah Univ, Coll Comp & Informat, Comp Sci Dept, Sharjah, U Arab Emirates

[2] Univ Oulu, Fac Informat Technol & Elect Engn, Ctr Wireless Commun, Oulu 90570, Finland

[3] Western Univ, Innovat Ctr Informat Engn, London N6A 3K7, ON, Canada

[4] Mohamed Bin Zayed Univ Artificial Intelligence MBZ, Machine Learning Dept, Abu Dhabi, U Arab Emirates

来源：

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY | 2024年 / 5卷

关键词：

Special issues and sections; 6G mobile communication; Federated learning; Machine learning; Intelligent networks; Deep learning;

D O I：

10.1109/OJCOMS.2023.3348029

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Collaborative machine learning is considered as the bedrock of the intelligent B5G networks, where distributed agents collaborate with each other to train learning models in a distributed fashion, without sharing data at a central entity. Despite its broad applicability, the main issue of collaborative learning is the need of local computing to build local learning models as well as iterative information exchange among agents, which may lead to high resource overhead unaffordable in many practical resource-limited systems such as unmanned aerial vehicles (UAVs) and Internet of Things (IoT). To alleviate this resource issue, it is essential to devise resource-efficient collaborative learning techniques, that can optimize the resource overhead in terms of communication, computing, and energy cost, and hence achieve satisfactory optimization/learning performance simultaneously.

引用

页码：1026 / 1028

页数：3

共 11 条

[1]

Charatsaris P., Diamanti M., Papavassiliou S., Joint user association and resource allocation for hierarchical federated learning based on games in satisfaction form, IEEE Open J. Commun. Soc., 5, pp. 457-471, (2024)

[2]

Yeh S.-P., Bhattacharya S., Sharma R., Moustafa H., Deep learning for intelligent and automated network slicing in 5G Open RAN (ORAN) deployment, IEEE Open J. Commun. Soc., 5, pp. 64-70, (2024)

[3]

Said A.M., Marot M., Afifi H., Moungla H., Optimal mobile IRS deployment for empowered 6G networks, IEEE Open J. Commun. Soc., 5, pp. 540-552, (2024)

[4]

Abbas K., Cho Y., Nauman A., Khan P.W., Khan T.A., Kondepu K., Convergence of AI and MEC for autonomous IoT service provisioning and assurance in B5G, IEEE Open J. Commun. Soc., 4, pp. 2913-2929, (2023)

[5]

Diamanti M., Kapsalis G., Tsiropoulou E.E., Papavassiliou S., Energy-efficient rate-splitting multiple access: A deep reinforcement learning-based framework, IEEE Open J. Commun. Soc., 4, pp. 2397-2409, (2023)

[6]

Selvarajan S., Manoharan H., Khadidos A.O., Shankar A., Mekala M.S., Khadidos A.O., RLIS: Resource limited improved security beyond fifth-generation networks using deep learning algorithms, IEEE Open J. Commun. Soc., 4, pp. 2383-2396, (2023)

[7]

Serhani M.A., Abreha H.G., Tariq A., Hayajneh M., Xu Y., Hayawi K., Dynamic data sample selection and scheduling in edge federated learning, IEEE Open J. Commun. Soc., 4, pp. 2133-2149, (2023)

[8]

Moudoud H., Cherkaoui S., Empowering security and trust in 5G and beyond: A deep reinforcement learning approach, IEEE Open J. Commun. Soc., 4, pp. 2410-2420, (2023)

[9]

Gururaj H.L., Natarajan R., Almujally N.A., Flammini F., Krishna S., Gupta S.K., Collaborative energy-efficient routing protocol for sustainable communication in 5G/6G wireless sensor networks, IEEE Open J. Commun. Soc., 4, pp. 2050-2061, (2023)

[10]

Yang F., Yang C., Huang J., Yu K., Garg S., Alrashoud M., Hypergraph based resource-efficient collaborative reinforcement learning for B5G massive IoT, IEEE Open J. Commun. Soc., 4, pp. 2439-2450, (2023)

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