Low Earth Orbit (LEO) constellations, each comprising a large number of satellites, have become a new source of big data "from the sky". Downloading such data to a ground station (GS) for big data analytics demands very high bandwidth and involves large propagation delays. Federated Learning (FL) offers a promising solution because it allows data to stay in-situ (never leaving satellites) and it only needs to transmit machine learning model parameters (trained on the satellites' data). However, the conventional, synchronous FL process can take several days to train a single FL model in the context of satellite communication (Satcom), due to a bottleneck caused by straggler satellites. In this paper, we propose an asynchronous FL framework for LEO constellations called AsyncFLEO to improve FL efficiency in Satcom. Not only does AsynFLEO address the bottleneck (idle waiting) in synchronous FL, but it also solves the issue of model staleness caused by straggler satellites. AsyncFLEO utilizes high-altitude platforms (HAPs) positioned "in the sky" as parameter servers, and consists of three technical components: (1) a ring-of-stars communication topology, (2) a model propagation algorithm, and (3) a model aggregation algorithm with satellite grouping and staleness discounting. Our extensive evaluation with both IID and non-IID data shows that AsyncFLEO outperforms the state of the art by a large margin, cutting down convergence delay by 22 times and increasing accuracy by 40%.
翻译:由大量卫星组成的低地球轨道(LEO)星座,每个由大量卫星组成的低地球轨道(LEO)星座,都已成为“从天上”进行大型数据的新来源。将这些数据下载到一个地面站(GS),用于大数据分析,要求非常高的带宽,并涉及大量的传播延迟。联邦学习(FL)提供了一个有希望的解决办法,因为它允许数据留在原地(永远不要离开卫星),而它只需要传输机器学习模型参数(在卫星数据上培训)。然而,常规、同步的FLL进程可能需要数天才能在卫星通讯(Satcom)中训练一个40级FL模型。由于斯特格勒卫星造成的瓶颈,将这些数据下载到地面站(GS),要求非常高的FLFL框架要求非常高的带宽,要求使用LEO(AsyncFLEO)来提高Satcom的FL效率。AFLEO不仅在同步的FLLL(等待)模型中解决瓶颈(等待),而且还需要用Stralergering State Stal Stal lemental Slations) lifilding Slational 3 Squlevations laft Squlations laft laft laft laft laft laft:“Slations slations” laft laft laftaldaldal laftaldaldal laftaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldalds lax laxmessaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldsmaldsmaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldals ladaldaldaldals