Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services (2305.05463v2)
Abstract: In the ever-expanding landscape of the IoT, managing the intricate network of interconnected devices presents a fundamental challenge. This leads us to ask: "What if we invite the IoT devices to collaboratively participate in real-time network management and IoT data-handling decisions?" This inquiry forms the foundation of our innovative approach, addressing the burgeoning complexities in IoT through the integration of NTN architecture, in particular, VHetNet, and an MT-HFL framework. VHetNets transcend traditional network paradigms by harmonizing terrestrial and non-terrestrial elements, thus ensuring expansive connectivity and resilience, especially crucial in areas with limited terrestrial infrastructure. The incorporation of MT-HFL further revolutionizes this architecture, distributing intelligent data processing across a multi-tiered network spectrum, from edge devices on the ground to aerial platforms and satellites above. This study explores MT-HFL's role in fostering a decentralized, collaborative learning environment, enabling IoT devices to not only contribute but also make informed decisions in network management. This methodology adeptly handles the challenges posed by the non-IID nature of IoT data and efficiently curtails communication overheads prevalent in extensive IoT networks. Significantly, MT-HFL enhances data privacy, a paramount aspect in IoT ecosystems, by facilitating local data processing and limiting the sharing of model updates instead of raw data. By evaluating a case-study, our findings demonstrate that the synergistic integration of MT-HFL within VHetNets creates an intelligent network architecture that is robust, scalable, and dynamically adaptive to the ever-changing demands of IoT environments. This setup ensures efficient data handling, advanced privacy and security measures, and responsive adaptability to fluctuating network conditions.
- M. Stoyanova, Y. Nikoloudakis, S. Panagiotakis, E. Pallis, and E. K. Markakis, “A survey on the internet of things (IoT) forensics: Challenges, approaches, and open issues,” IEEE Commun. Surv. Tut., vol. 22, no. 2, pp. 1191-1221, Secondquarter 2020.
- Y. Cui, F. Liu, X. Jing, and J. Mu, “Integrating sensing and communications for ubiquitous IoT: Applications, trends, and challenges,” IEEE Network, vol. 35, no. 5, pp. 158-167, Oct. 2021.
- M. Alzenad and H. Yanikomeroglu, “Coverage and rate analysis for vertical heterogeneous networks (VHetNets),” IEEE Trans. Wireless Commun., vol. 18, no. 12, pp. 5643-5657, Dec. 2019.
- X. Cao, P. Yang, M. Alzenad, X. Xi, D. Wu and H. Yanikomeroglu, “Airborne communication networks: A survey,” IEEE J. Sel. Areas Commun., vol. 36, no. 9, pp. 1907-1926, Sept. 2018.
- A. Farajzadeh, A. Yadav, O. Abbasi, W. Jaafar, and H. Yanikomeroglu, “FLSTRA: Federated learning in stratosphere,” IEEE Trans. Wireless Commun. (Early Access), 2023.
- T. Darwish, G. K. Kurt, H. Yanikomeroglu, G. Senarath, and P. Zhu, “A vision of self-Evolving network management for future intelligent vertical HetNet,” IEEE Wireless Commun., vol. 28, no. 4, pp. 96-105, Aug. 2021.
- D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for internet of things: A comprehensive survey,” IEEE Commun. Surv. Tut., vol. 23, no. 3, pp. 1622-1658, Thirdquarter 2021.
- F. C. Orlandi, J. C. S. Dos Anjos, V. R. Q. Leithardt, J. F. De Paz Santana, and C. F. R. Geyer, “Entropy to mitigate non-IID data problem on federated learning for the edge intelligence environment,” IEEE Access, vol. 11, pp. 78845-78857, 2023.
- S. K. Sharma and X. Wang, “Toward massive machine type communications in ultra-dense cellular IoT networks: Current issues and machine learning-assisted solutions,” IEEE Commun. Surv. Tut., vol. 22, no. 1, pp. 426-471, Firstquarter 2020.
- A. Farajzadeh, M. G. Khoshkholgh, H. Yanikomeroglu, and O. Ercetin, “Self-evolving integrated vertical heterogeneous networks,” IEEE Open J. Commun. Soc, vol. 4, pp. 552-580, 2023.
- L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, “Federated learning for internet of things: Recent advances, taxonomy, and open challenges” IEEE Commun. Surv. Tut., vol. 23, no. 3, pp. 1759-1799, Thirdquarter 2021.
- M. S. H. Abad, E. Ozfatura, D. Gunduz, and O. Ercetin, “Hierarchical federated learning across heterogeneous cellular networks,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Barcelona, Spain, 2020, pp. 8866-8870.
- Z. Zhong, W. Bao, J. Wang, X. Zhu, and X. Zhang, “FLEE: A hierarchical federated learning framework for distributed deep neural network over cloud, edge, and end device,” ACM Trans. Intell. Syst. Technol., vol. 13, no. 71, pp. 1-24, Oct. 2022.
- W. Khawaja, I. Guvenc, D. W. Matolak, U. C. Fiebig, and N. Schneckenburger, “A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles,” IEEE Commun. Surv. Tut., vol. 21, no. 3, pp. 2361-2391, Thirdquarter 2019.