In this paper, we investigate the problem of scheduling transmissions for spatially scattered nodes that contribute to a collaborative federated learning (FL) algorithm via wireless links provided by a drone. In the considered system, the drone acts as an orchestrator, coordinating the transmissions and the learning schedule within a predefined deadline. The actual schedule is reflected in a planned path: as the drone traverses it, it controls the distance and thereby the data rate to each node. Hence, the model is structured such that the drone orchestrator uses the path (trajectory) as its only tool to achieve fairness in terms of learning staleness , which reflects the learning time discrepancy among the nodes. Using the number of learning epochs performed at each learner as a performance indicator, we combine the average number of epochs computed and staleness into a balanced optimization criterion that is agnostic to the underlying FL implementation. We consider two methods for solving the complex trajectory planning optimization problem for static nodes: (1) successive convex programming (SCP) and (2) deep reinforcement learning (RL). Considering the proposed criterion, both methods are compared in three specific scenarios with few nodes. The results show that drone-orchestrated FL outperforms an immobile deployment by providing improvements in the range of 57% to 87.7%. Additionally, RL-guided trajectories are generally superior to SCP provided ones for complex node arrangements.

中文翻译：

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与无人机协调器的联合学习：最大限度地减少陈旧性的路径规划

在本文中，我们研究了通过无人机提供的无线链接为空间分散的节点调度传输的问题，这些节点有助于协作式联合学习（FL）算法。在考虑的系统中，无人机充当协调器，在预定的期限内协调传输和学习计划。实际的时间表反映在计划的路径中：随着无人机的穿越，它控制着距离，从而控制了到每个节点的数据速率。因此，该模型的结构使得无人机协调器将路径（轨迹）用作实现学习公平性的唯一工具过时 ，这反映了节点之间的学习时间差异。使用在每个学习者处执行的学习时期数作为性能指标，我们将计算出的平均时期数和陈旧性结合到一个平衡的优化标准中，该标准与基本的FL实现无关。我们考虑了两种解决静态节点的复杂轨迹规划优化问题的方法：（1）连续凸规划（SCP）和（2）深度强化学习（RL）。考虑到提出的标准，将这两种方法在节点较少的三种特定情况下进行比较。结果表明，无人驾驶飞行器通过提供57％到87.7％的改进而胜过固定部署。此外，对于复杂的节点排列，RL引导的轨迹通常优于SCP提供的轨迹。