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Fed-Haul: A Federated Learning Dual Band Point Multi-Point Backhaul Requirements in 5G Evolution and Beyond

Authors:

Abdellah Chehri,

Hasna Chaibi,

Abderrahmane Mettiti,

Rachid Saadane,

Gwanggil JeonAuthors Info & Claims

NISS '23: Proceedings of the 6th International Conference on Networking, Intelligent Systems & Security

Article No.: 59, Pages 1 - 5

https://doi.org/10.1145/3607720.3607783

Published: 13 November 2023 Publication History

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Abstract

The rapid adoption of smartphones and the explosive growth of data traffic due to these devices have been phenomenal. As the world anticipates more connected devices — the Internet of Things (IoT), vehicle-to-vehicle (V2V) communications, and wearable devices — and more value-added applications and services (ultra-high-definition video, 360° video, virtual reality, smart cars, etc.), leading industry experts are calling for the sixth generation (6G) networks. Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. Federated learning (FL) is essential in optimizing wireless communication networks' resources. On the other hand, wireless communications are crucial for FL. Therefore, the purpose of this survey paper is to bridge this gap in the literature by discussing the interdependency between FL and backhaul wireless communications.

References

[1]

Liu L., Zhang J., Song S., Letaief K. B., Edge-assisted hierarchical federated learning with non-iid data, arXiv preprint arXiv:1905.06641, 2019.

Google Scholar

[2]

Jiang Y., Wang S., Victor V., Jun K., Lee W., Leung K., Leandros T., Model pruning enables efficient federated learning on edge devices, IEEE Transactions on Neural Networks and Learning Systems, 2022.

Google Scholar

[3]

Mohammed I., Tabatabai, S. A. Fuqaha, F. Bouanani, J. Qadir, B. Qolomany, M. Guizani. Budgeted online selection of candidate IoT clients to participate in federated learning, IEEE Internet of Things Journal, VOL. 7, NO. 8, pp. 5938-5952, 2020.

Google Scholar

[4]

Wang L., Wang W., Li B. CMFL: Mitigating communication overhead for federated learning, IEEE 39th international conference on distributed computing systems, ICDCS, pp. 954-964, 2019

Crossref

Google Scholar

[5]

Ping-Heng Kuo and Mourad A., Millimeter wave for 5G mobile fronthaul and backhaul, 2017 European Conference on Networks and Communications (EuCNC), 2017, pp. 1-5.

Crossref

Google Scholar

[6]

Bande M. and Veeravalli V. V., Design of a Heterogeneous Cellular Network with a Wireless Backhaul, in IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 243-253, Jan. 2021.

Digital Library

Google Scholar

[7]

Chehri A. and Mouftah H. T., New MMSE Downlink Channel Estimation for Sub-6 GHz Non-Line-of-Sight Backhaul, 2018 IEEE Globecom Workshops (GC Wkshps), 2018, pp. 1-7.

Crossref

Google Scholar

[8]

Chehri A. and Mouftah H. T., PHY-MAC MIMO Precoder Design for Sub-6 GHz Backhaul Small Cell, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020, pp. 1-5.

Crossref

Google Scholar

[9]

Su X. and Chang K., A comparative study on wireless backhaul solutions for beyond 4G network, The International Conference on Information Networking 2013 (ICOIN), 2013, pp. 505-51

Digital Library

Google Scholar

[10]

Yang, Y.; Hong, Y.; Park, J. Efficient Gradient Updating Strategies with Adaptive Power Allocation for Federated Learning over Wireless Backhaul. Sensors 2021, 21, 6791. https://doi.org/10.3390/s21206791

Crossref

Google Scholar

[11]

Lin Z., Li X., Lau V. K. N., Gong Y. and Huang K., Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis, in IEEE Transactions on Wireless Communications, vol. 21, no. 3, pp. 1542-1556, March 2022.

Crossref

Google Scholar

[12]

Niknam S., Dhillon H. S. and Reed J. H., "Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges, in IEEE Communications Magazine, vol. 58, no. 6, pp. 46-51, June 2020.

Crossref

Google Scholar

[13]

Vu T. T., Ngo D. T., Tran N. H., Ngo H. Q., Dao M. N. and Middleton R. H., Cell-Free Massive MIMO for Wireless Federated Learning, in IEEE Transactions on Wireless Communications, vol. 19, no. 10, pp. 6377-6392, Oct. 2020.

Crossref

Google Scholar

[14]

Chehri A., Utility-Based Beam Selection Algorithms for Sub-6 GHz Backhauls, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022, pp. 1682-1683.

Crossref

Google Scholar

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Liu BBlancaflor E(2024)Research on federal learning privacy protection based on secure multi-party computingProceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy10.1145/3672919.3672947(142-147)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1145/3672919.3672947

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Fed-Haul: A Federated Learning Dual Band Point Multi-Point Backhaul Requirements in 5G Evolution and Beyond

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NISS '23: Proceedings of the 6th International Conference on Networking, Intelligent Systems & Security

May 2023

451 pages

ISBN:9798400700194

DOI:10.1145/3607720

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Published: 13 November 2023

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NISS 2023: The 6th International Conference on Networking, Intelligent Systems & Security

May 24 - 26, 2023

Larache, Morocco

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Liu BBlancaflor E(2024)Research on federal learning privacy protection based on secure multi-party computingProceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy10.1145/3672919.3672947(142-147)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1145/3672919.3672947

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Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review

Multi-Layer Collaborative Federated Learning architecture for 6G Open RAN

Federated Machine Learning In 5G Smart Healthcare: A Security Perspective Review

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