FedHAP: Fast Federated Learning for LEO Constellations using Collaborative HAPs
FedHAP: Fast Federated Learning for LEO Constellations using Collaborative HAPs
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DOI：
10.1109/wcsp55476.2022.10039157
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Mohamed Elmahallawy;Tie Luo
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文献类型：
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作者：
Mohamed Elmahallawy;Tie Luo
在过去的几年里，低地球轨道（LEO）卫星星座的部署激增，因为它们能够提供宽带互联网接入，并收集大量可用于在全球范围内开发人工智能的地球观测数据。由于传统的机器学习（ML）方法通过将卫星数据下载到地面站（GS）来训练模型并不实用，因此联邦学习（FL）提供了一种潜在的解决方案。然而，现有的FL方法不能很容易地应用，因为它们的过度延长的训练时间所造成的具有挑战性的卫星GS通信环境。本文提出了FedHAP，它将高空平台（HAP）作为分布式参数服务器（PS）引入FL用于卫星通信（或更具体地说，LEO星座），以实现快速有效的模型训练。FedHAP由三个部分组成：1）分层通信架构，2）模型分发算法，3）模型聚合算法。我们广泛的模拟表明，与最先进的基线相比，FedHAP显著加速了FL模型的收敛，将训练时间从几天缩短到几个小时，同时实现了更高的准确性。
Low Earth Orbit (LEO) satellite constellations have seen a surge in deployment over the past few years by virtue of their ability to provide broadband Internet access as well as to collect vast amounts of Earth observational data that can be utilized to develop AI on a global scale. As traditional machine learning (ML) approaches that train a model by downloading satellite data to a ground station (GS) are not practical, Federated Learning (FL) offers a potential solution. However, existing FL approaches cannot be readily applied because of their excessively prolonged training time caused by the challenging satellite-GS communication environment. This paper proposes FedHAP, which introduces high-altitude platforms (HAPs) as distributed parameter servers (PSs) into FL for Satcom (or more concretely LEO constellations), to achieve fast and efficient model training. FedHAP consists of three components: 1) a hierarchical communication architecture, 2) a model dissemination algorithm, and 3) a model aggregation algorithm. Our extensive simulations demonstrate that FedHAP significantly accelerates FL model convergence as compared to state-of-the-art baselines, cutting the training time from several days down to a few hours, yet achieving higher accuracy.