In this paper, we study asynchronous federated learning (FL) in a wireless distributed learning network (WDLN). To allow each edge device to use its local data more efficiently via asynchronous FL, transmission scheduling in the WDLN for asynchronous FL should be carefully determined considering system uncertainties, such as time-varying channel and stochastic data arrivals, and the scarce radio resources in the WDLN. To address this, we propose a metric, called an effectivity score, which represents the amount of learning from asynchronous FL. We then formulate an Asynchronous Learning-aware transmission Scheduling (ALS) problem to maximize the effectivity score and develop three ALS algorithms, called ALSA-PI, BALSA, and BALSA-PO, to solve it. If the statistical information about the uncertainties is known, the problem can be optimally and efficiently solved by ALSA-PI. Even if not, it can be still optimally solved by BALSA that learns the uncertainties based on a Bayesian approach using the state information reported from devices. BALSA-PO suboptimally solves the problem, but it addresses a more restrictive WDLN in practice, where the AP can observe a limited state information compared with the information used in BALSA. We show via simulations that the models trained by our ALS algorithms achieve performances close to that by an ideal benchmark and outperform those by other state-of-the-art baseline scheduling algorithms in terms of model accuracy, training loss, learning speed, and robustness of learning. These results demonstrate that the adaptive scheduling strategy in our ALS algorithms is effective to asynchronous FL.
翻译:在本文中,我们在一个无线分布式学习网络(WDLN)中研究非同步的联结学习(FL)(FL ) 。 为了让每个边缘装置能够通过无同步式FL更有效地使用其本地数据,应当仔细确定WDLN中无同步FL的传输进度,考虑到系统不确定性,例如时间变化频道和数据到达,以及WDLN的稀少的无线电资源。为了解决这个问题,我们建议采用一个计量,称为效果性能评分,它代表着从非同步式FLL的学习数量。然后,我们设计一个“A-A-A-aware”自动传输系统(ALS)问题,以最大限度地提高效果分数,并开发三种ALS 算法,称为AL-PL的传输进度表,以解决这个问题。如果了解有关不确定性的统计资料,那么问题可以通过AL-PI的模型得到最佳和有效的解决。即使不是如此,BALSA的精确的算法,但是通过BALSA(B-L)的精确度(BA-L)的精确度(SA-L)方法,在BA-L)的精细化的精细化的精确度上,通过一个精确的精确的精确的精确的精确度上,通过一个测试的精确度,通过一个测试的精确的精确的精确的精确度,通过一个测试的精确的精确的精确的计算方法,从一个精确的精确度的精确度,从一个精确的计算方法学习到一个精确度,我们测测算方法,我们测算方法,从一个精确的精确的精确的精确的精确的精确的精确的精确的计算方法,从一个精确的计算方法,从一个精确的精确的精确的精确的精确的计算方法,从一个测试的精确的精确的计算方法,从一个精确的精确的精确的计算,从一个精确的计算方法,从一个测量的计算,从一个测试的计算方法学习到我们所使用的的精确的精确的计算方法,从一个精确的精确的精确的计算中学习到我们所使用的的计算中学习到我们所使用的的精确的计算。