论文标题
无人用的网络网络分散学习
UAV-Aided Decentralized Learning over Mesh Networks
论文作者
论文摘要
分散的学习使无线网络设备能够协作训练机器学习（ML）模型，该模型仅依靠设备到设备（D2D）通信。众所周知，分散优化算法的收敛速度严重取决于网络连接的程度，而较密集的网络拓扑导致收敛时间较短。因此，由于其无线节点的通信范围有限，现实世界网络网络的局部连通性破坏了分散学习协议的效率，从而使它们可能不切实际。在这项工作中，我们调查了在这种挑战性条件下促进分散的学习程序中无人驾驶飞机（UAV）（UAV）的作用。我们提出了一个优化的无人机轨迹，该轨迹被定义为无人机依次访问的一系列航路点，以便在稀疏连接的用户组中传输智能。然后，我们提供了一系列实验，强调了无人机在对网络网络的分散学习中的基本作用。
Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication. It is known that the convergence speed of decentralized optimization algorithms severely depends on the degree of the network connectivity, with denser network topologies leading to shorter convergence time. Consequently, the local connectivity of real world mesh networks, due to the limited communication range of its wireless nodes, undermines the efficiency of decentralized learning protocols, rendering them potentially impracticable. In this work we investigate the role of an unmanned aerial vehicle (UAV), used as flying relay, in facilitating decentralized learning procedures in such challenging conditions. We propose an optimized UAV trajectory, that is defined as a sequence of waypoints that the UAV visits sequentially in order to transfer intelligence across sparsely connected group of users. We then provide a series of experiments highlighting the essential role of UAVs in the context of decentralized learning over mesh networks.
