无人机联邦学习联合优化轨迹与资源分配，显著降低系统延迟。
Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication
- 通过块坐标下降与逐次凸逼近法联合优化无人机轨迹和资源分配。
- 仿真显示相比基准方案，系统延迟最高降低68.54%。
- 适合研究无人机网络、边缘智能与联邦学习融合的学者参考。
本文研究了基于无人飞行器（UAV）的集成感知与通信系统中联邦学习（FL）的新型延迟优化问题。在此架构中，分布式无人机利用感知数据参与模型训练，并与作为联邦学习聚合器的基站（BS）协作构建全局模型。目标是通过联合优化无人机轨迹及无人机与基站的资源分配，最小化整个联邦学习系统的延迟。由于该优化问题具有非凸性，求解困难。为此，我们提出一种简单高效的迭代算法，结合块坐标下降与逐次凸逼近技术，获得高质量近似解。仿真结果表明，在实际参数设置下，所提联合优化策略有效，相较基准方案最多可降低系统延迟68.54%。
原文摘要 · Abstract (English)
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.
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