论文标题
无人用的层次汇总聚集
UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning
论文作者
论文摘要
随着大量数据在移动边缘的爆炸性增加，空中联邦学习（OA-FL）成为一种有希望的技术，可降低通信成本和隐私泄漏风险。但是，当相对较大的区域中的设备合作训练机器学习模型时，随之而来的Straggler问题将大大降低学习性能。在本文中，我们提出了一个无人驾驶飞机（UAV）辅助OA-FL系统，在该系统中，无人机充当参数服务器（PS），以汇总本地梯度以层次结构用于全局模型更新。在这个无人机辅助层次聚合方案下，我们进行了梯度相关 - 感知的FL性能分析。然后，我们根据分析结果制定平均平方误差（MSE）最小化问题，以调整UAV轨迹和全局聚合系数。开发了基于交替优化（AO）和连续的凸近似（SCA）的算法来解决公式的问题。仿真结果证明了我们无人机辅助分层聚合方案的巨大潜力。
With huge amounts of data explosively increasing in the mobile edge, over-the-air federated learning (OA-FL) emerges as a promising technique to reduce communication costs and privacy leak risks. However, when devices in a relatively large area cooperatively train a machine learning model, the attendant straggler issues will significantly reduce the learning performance. In this paper, we propose an unmanned aerial vehicle (UAV) assisted OA-FL system, where the UAV acts as a parameter server (PS) to aggregate the local gradients hierarchically for global model updating. Under this UAV-assisted hierarchical aggregation scheme, we carry out a gradient-correlation-aware FL performance analysis. We then formulate a mean squared error (MSE) minimization problem to tune the UAV trajectory and the global aggregation coefficients based on the analysis results. An algorithm based on alternating optimization (AO) and successive convex approximation (SCA) is developed to solve the formulated problem. Simulation results demonstrate the great potential of our UAV-assisted hierarchical aggregation scheme.
