Abstract
A cornerstone of wireless connectivity involves trust and privacy in the data shared between users and network elements as wireless connectivity becomes an integrated, fundamental element of society. With a large influx of data in Beyond 5G (B5G) systems from end-users and network elements, it is imperative to understand how data is collected and used for real-time data processing operations. The current wireless network learning involves centralizing the training data, which is inefficient as it continuously requires end devices to send their collected data to a central server. Federated Learning (FL) effectively allows end devices to train ground-truth data on-device, and only model update parameters are sent back to the federated server. This work proposes a Chameleon FL model, FED6G, for network slicing in 5G and Beyond systems to solve complex resource optimization problems without collecting sensitive, confidential information from end devices. The evaluation results reflect more than 39% improvement in Mean Squared Error (MSE), 46% better model accuracy, and more than 23% reduced energy cost for training the proposed FED6G against the traditional deep learning neural network model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Network Slicing—3GPP Use Case. 1 Oct. 2020, tools.ietf.org/id/draft-defoy-netslices-3gpp-network-slicing-02.html
H.B. McMahan, et al., Communication-efficient learning of deep networks from decentralized data (2016). https://doi.org/10.48550/arXiv.1602.05629
T. Taleb, I. Afolabi, K. Samdanis, F.Z. Yousaf, On multi-domain network slicing orchestration architecture and federated resource control. IEEE Netw. 33(5), 242–252 (2019). https://doi.org/10.1109/MNET.2018.1800267
A. Thantharate, R. Paropkari, V. Walunj, C. Beard, DeepSlice: a deep learning approach towards an efficient and reliable network slicing in 5G networks, in IEEE 10th Annual Ubiquitous Computing. Electronics & Mobile Communication Conference (UEMCON), vol. 2019 (2019), pp. 0762–0767. https://doi.org/10.1109/UEMCON47517.2019.8993066
A. Thantharate, R. Paropkari, V. Walunj, C. Beard, P. Kankariya, Secure5G: a deep learning framework towards a secure network slicing in 5G and beyond, in 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) (2020), pp. 0852–0857. https://doi.org/10.1109/CCWC47524.2020.9031158
Y. Liu, X. Yuan, Z. Xiong, J. Kang, X. Wang, D. Niyato, Federated learning for 6G communications: challenges, methods, and future directions. China Commun. 17(9), 105–118 (2020). https://doi.org/10.23919/JCC.2020.09.009
S. Niknam, H.S. Dhillon, J.H. Reed, Federated learning for wireless communications: motivation, opportunities, and challenges. IEEE Commun. Mag. 58(6), 46–51 (2020). https://doi.org/10.1109/MCOM.001.1900461
Z. Yu et al., Federated learning based proactive content caching in edge computing. IEEE Global Commun. Conf. (GLOBECOM) 2018, 1–6 (2018). https://doi.org/10.1109/GLOCOM.2018.8647616
C. Zhang, P. Patras, H. Haddadi, Deep learning in mobile and wireless networking: a survey. IEEE Commun. Surv. Tutor. 21(3), pp. 2224–2287, thirdquarter (2019). https://doi.org/10.1109/COMST.2019.2904897
G. Wang, G. Feng, T.Q.S. Quek, S. Qin, R. Wen, W. Tan, Reconfiguration in network slicing-optimizing the profit and performance. IEEE Trans. Netw. Serv. Manag. 16(2), 591–605 (2019). https://doi.org/10.1109/TNSM.2019.2899609
N.H. Tran, W. Bao, A. Zomaya, M.N.H. Nguyen, C.S. Hong, Federated learning over wireless networks: optimization model design and analysis, in IEEE INFOCOM 2019—IEEE Conference on Computer Communications (2019), pp. 1387–1395, https://doi.org/10.1109/INFOCOM.2019.8737464
W. Shi, S. Zhou, Z. Niu, M. Jiang, L. Geng, Joint device scheduling and resource allocation for latency constrained wireless federated learning. IEEE Trans. Wirel. Commun. 20(1), 453–467 (2021). https://doi.org/10.1109/TWC.2020.3025446
R. Fantacci, B. Picano, When network slicing meets prospect theory: a service provider revenue maximization framework. IEEE Trans. Veh. Technol. 69(3), 3179–3189 (2020). https://doi.org/10.1109/TVT.2019.2963462
Q. Xu, J. Wang, K. Wu, Learning-based dynamic resource provisioning for network slicing with ensured end-to-end performance bound. IEEE Trans. Netw. Sci. Eng. 7(1), pp. 28–41 (2020). https://doi.org/10.1109/TNSE.2018.2876918
B. Brik, A. Ksentini, On predicting service-oriented network slices performances in 5g: a federated learning approach, in 2020 IEEE 45th Conference on Local Computer Networks (LCN) (2020), pp. 164–171. https://doi.org/10.1109/LCN48667.2020.9314849
S. Yu, X. Chen, Z. Zhou, X. Gong, D. Wu, When deep reinforcement learning meets federated learning: intelligent multitimescale resource management for multiaccess edge computing in 5G ultradense network. IEEE Internet Things J. 8(4), 2238–2251 (2021). https://doi.org/10.1109/JIOT.2020.3026589.
T. Subramanya, R. Riggio, Centralized and federated learning for predictive VNF autoscaling in multi-domain 5G networks and beyond. IEEE Trans. Netw. Serv. Manag. 18(1), 63–78 (2021). https://doi.org/10.1109/TNSM.2021.3050955
Federated learning for 5G and beyond systems (2022). https://www.kaggle.com/datasets/anuragthantharate/federate-learning-for-5g-and-beyond-systems
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Thantharate, A. (2023). FED6G: Chameleon Learning for Network Slice Management in Beyond 5G Systems. In: Thampi, S.M., Mukhopadhyay, J., Paprzycki, M., Li, KC. (eds) International Symposium on Intelligent Informatics. ISI 2022. Smart Innovation, Systems and Technologies, vol 333. Springer, Singapore. https://doi.org/10.1007/978-981-19-8094-7_32
Download citation
DOI: https://doi.org/10.1007/978-981-19-8094-7_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8093-0
Online ISBN: 978-981-19-8094-7
eBook Packages: EngineeringEngineering (R0)