Spatio-Temporal Federated Learning for Massive Wireless Edge Networks
Spatio-Temporal Federated Learning for Massive Wireless Edge Networks
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DOI：
10.1109/icc45855.2022.9838401
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Chun-Hung Liu;Kai-Ten Feng;Luwei Wei;Yung-Jie Luo
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文献类型：
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作者：
Chun-Hung Liu;Kai-Ten Feng;Luwei Wei;Yung-Jie Luo
本文提出了一种在大规模无线边缘网络上进行高效联合学习的新方法，其中边缘服务器和众多移动的设备（客户端）共同学习全局模型，而无需将移动的设备收集的大量数据传输到边缘服务器。所提出的FL方法被称为时空FL（STFL），其联合利用来自被调度为在各种训练时期中加入STFL的不同移动的设备的学习更新之间的空间和时间相关性。STFL模型不仅代表了由于数据传输中断而从边缘服务器到移动的设备的实际间歇学习行为，而且还具有补偿损失学习更新的机制，以减轻间歇学习的影响。提出了一个STFL的分析框架，并通过其收敛性能研究了STFL的学习能力。特别是，我们评估了数据传输中断，间歇性学习缓解和数据集的统计异质性对STFL收敛性能的影响。研究结果为基于STFL的无线网络的设计和分析提供了重要的见解。
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.