This paper presents a novel approach to conduct highly efficient federated learning (FL) over a massive wireless edge network, where an edge server and numerous mobile devices (clients) jointly learn a global model without transporting the huge amount of data collected by the mobile devices to the edge server. The proposed FL approach is referred to as spatio-temporal FL (STFL), which jointly exploits the spatial and temporal correlations between the learning updates from different mobile devices scheduled to join STFL in various training epochs. The STFL model not only represents the realistic intermittent learning behavior from the edge server to the mobile devices due to data delivery outage, but also features a mechanism of compensating loss learning updates in order to mitigate the impacts of intermittent learning. An analytical framework of STFL is proposed and employed to study the learning capability of STFL via its convergence performance. In particular, we have assessed the impact of data delivery outage, intermittent learning mitigation, and statistical heterogeneity of datasets on the convergence performance of STFL. The results provide crucial insights into the design and analysis of STFL based wireless networks.
翻译:本文介绍了在大型无线边缘网络上开展高效联合学习(FL)的新做法,在这种网络中,边缘服务器和许多移动设备(客户)共同学习一个全球模型,而不将移动设备收集的大量数据传送到边缘服务器。拟议的FL方法被称为spatio-temoor FL(STFL),它共同利用了预定加入STFL的各种培训时代的不同移动设备之间最新的学习空间和时间关系。STFL模型不仅代表了边缘服务器到移动设备之间的现实的间歇学习行为,因为数据传送中断,而且还包含一种补偿性损失学习更新的机制,以减轻间歇学习的影响。提议并使用STLLL的分析框架,通过其趋同性能研究STFL的学习能力。特别是,我们评估了数据传送中断、间歇学习减缓和数据集的统计多样性对STFL的融合性能的影响。结果为基于STFL的无线网络的设计和分析提供了至关重要的洞察。