In this work, we investigate the problem of an online trajectory design for
an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where
several different communities exist, each defined by a unique task to be
learned. In this setting, spatially distributed devices belonging to each
community collaboratively contribute towards training their community model via
wireless links provided by the UAV. Accordingly, the UAV acts as a mobile
orchestrator coordinating the transmissions and the learning schedule among the
devices in each community, intending to accelerate the learning process of all
tasks. We propose a heuristic metric as a proxy for the training performance of
the different tasks. Capitalizing on this metric, a surrogate objective is
defined which enables us to jointly optimize the UAV trajectory and the
scheduling of the devices by employing convex optimization techniques and graph
theory. The simulations illustrate the out-performance of our solution when
compared to other handpicked static and mobile UAV deployment baselines.

本研究旨在探讨在联邦学习中，一种针对无人机的在线轨迹设计问题，其中多个不同的社区存在，每个社区被唯一的任务定义所学习。在这种情况下，每个社区的空间分布设备通过无人机提供的无线链接协作贡献于对其社区模型的培训。通过引入一种启发式指标，我们提出了使用凸优化技术和图论一起优化无人机轨迹和设备调度的代理目标，以加速所有任务的学习进程，并且通过模拟实验证明了我们的解决方案相对于其他手动选择的静态和移动无人机部署基线的卓越表现。

无人机辅助多团体联邦学习

UAV-Aided Multi-Community Federated Learning

Submodular function minimization is a fundamental optimization problem that
arises in several applications in machine learning and computer vision. The
problem is known to be solvable in polynomial time, but general purpose
algorithms have high running times and are unsuitable for large-scale problems.
Recent work have used convex optimization techniques to obtain very practical
algorithms for minimizing functions that are sums of ``simple" functions. In
this paper, we use random coordinate descent methods to obtain algorithms with
faster linear convergence rates and cheaper iteration costs. Compared to
alternating projection methods, our algorithms do not rely on full-dimensional
vector operations and they converge in significantly fewer iterations.

本文研究子模函数最小化问题，并采用随机坐标下降方法来获得具有更快线性收敛率和更低迭代成本的算法。与交替投影方法相比，我们的算法不依赖于完全维度的向量运算，并且收敛所需的迭代次数显著减少。