Delay Optimization of a Federated Learning-based UAV-aided IoT network
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916606005739520 |
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| author | Firouzjaei, Hossein Mohammadi Moghaddam, Javad Zeraatkar Ardebilipour, Mehrdad |
| author_facet | Firouzjaei, Hossein Mohammadi Moghaddam, Javad Zeraatkar Ardebilipour, Mehrdad |
| contents | This paper explores the integration of power splitting(PS) simultaneous wireless information and power transfer (SWIPT) architecture and federated learning (FL) in Internet of Things (IoT) networks. The use of SWIPT allows power-constrained devices to simultaneously harvest energy and transmit data, addressing the energy limitations faced by IoT devices. The proposed scenario involves an Unmanned Arial Vehicle (UAV) serving as the base station (BS) and edge server, aggregating weight updates from IoT devices and unicasting aggregated updates to each device. The results demonstrate the feasibility of FL in IoT scenarios, ensuring communication efficiency without depleting device batteries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06284 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Delay Optimization of a Federated Learning-based UAV-aided IoT network Firouzjaei, Hossein Mohammadi Moghaddam, Javad Zeraatkar Ardebilipour, Mehrdad Signal Processing This paper explores the integration of power splitting(PS) simultaneous wireless information and power transfer (SWIPT) architecture and federated learning (FL) in Internet of Things (IoT) networks. The use of SWIPT allows power-constrained devices to simultaneously harvest energy and transmit data, addressing the energy limitations faced by IoT devices. The proposed scenario involves an Unmanned Arial Vehicle (UAV) serving as the base station (BS) and edge server, aggregating weight updates from IoT devices and unicasting aggregated updates to each device. The results demonstrate the feasibility of FL in IoT scenarios, ensuring communication efficiency without depleting device batteries. |
| title | Delay Optimization of a Federated Learning-based UAV-aided IoT network |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2502.06284 |