Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services

• URL: http://arxiv.org/abs/2305.05463v2
• Date: Mon, 11 Dec 2023 18:53:55 GMT
• Title: Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services
• Authors: Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu
• Abstract summary: This study explores MT-HFL's role in fostering a decentralized, collaborative learning environment. It enables IoT devices to not only contribute but also make informed decisions in network management. This setup ensures efficient data handling, advanced privacy and security measures, and responsive to fluctuating network conditions.
• Score: 24.10349383347469
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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