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.

中文翻译：

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无线网络中异步联邦学习的自适应传输调度


在本文中，我们研究无线分布式学习网络（WDLN）中的异步联邦学习（FL）。为了允许每个边缘设备通过异步 FL 更有效地使用其本地数据，应仔细确定 WDLN 中用于异步 FL 的传输调度，并考虑系统不确定性，例如时变信道和随机数据到达，以及网络中稀缺的无线资源。 WDLN。为了解决这个问题，我们提出了一个称为有效性分数的指标，它代表异步 FL 的学习量。然后，我们制定异步学习感知传输调度 (ALS) 问题以最大化有效性分数，并开发三种 ALS 算法（称为 ALSA-PI、BALSA 和 BALSA-PO）来解决该问题。如果有关不确定性的统计信息已知，则可以通过 ALSA-PI 最优且高效地解决问题。即使不是，它仍然可以通过 BALSA 来优化解决，BALSA 使用设备报告的状态信息基于贝叶斯方法来学习不确定性。 BALSA-PO 次优地解决了该问题，但它在实践中解决了更具限制性的 WDLN，其中与 BALSA 中使用的信息相比，AP 可以观察到有限的状态信息。我们通过模拟表明，由我们的 ALS 算法训练的模型的性能接近理想基准，并且在模型准确性、训练损失、学习速度和鲁棒性方面优于其他最先进的基线调度算法。的学习。这些结果表明我们的 ALS 算法中的自适应调度策略对于异步 FL 是有效的。