Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910697111158784 |
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| author | Shaon, Shaba Nguyen, Tien Mohjazi, Lina Kaushik, Aryan Nguyen, Dinh C. |
| author_facet | Shaon, Shaba Nguyen, Tien Mohjazi, Lina Kaushik, Aryan Nguyen, Dinh C. |
| contents | This paper studies a new latency optimization problem in unmanned aerial vehicles (UAVs)-enabled federated learning (FL) with integrated sensing and communication. In this setup, distributed UAVs participate in model training using sensed data and collaborate with a base station (BS) serving as FL aggregator to build a global model. The objective is to minimize the FL system latency over UAV networks by jointly optimizing UAVs' trajectory and resource allocation of both UAVs and the BS. The formulated optimization problem is troublesome to solve due to its non-convexity. Hence, we develop a simple yet efficient iterative algorithm to find a high-quality approximate solution, by leveraging block coordinate descent and successive convex approximation techniques. Simulation results demonstrate the effectiveness of our proposed joint optimization strategy under practical parameter settings, saving the system latency up to 68.54\% compared to benchmark schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08918 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication Shaon, Shaba Nguyen, Tien Mohjazi, Lina Kaushik, Aryan Nguyen, Dinh C. Information Theory Artificial Intelligence Networking and Internet Architecture This paper studies a new latency optimization problem in unmanned aerial vehicles (UAVs)-enabled federated learning (FL) with integrated sensing and communication. In this setup, distributed UAVs participate in model training using sensed data and collaborate with a base station (BS) serving as FL aggregator to build a global model. The objective is to minimize the FL system latency over UAV networks by jointly optimizing UAVs' trajectory and resource allocation of both UAVs and the BS. The formulated optimization problem is troublesome to solve due to its non-convexity. Hence, we develop a simple yet efficient iterative algorithm to find a high-quality approximate solution, by leveraging block coordinate descent and successive convex approximation techniques. Simulation results demonstrate the effectiveness of our proposed joint optimization strategy under practical parameter settings, saving the system latency up to 68.54\% compared to benchmark schemes. |
| title | Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication |
| topic | Information Theory Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2411.08918 |