论文标题
无线网络上异步联合学习的调度和聚合设计
Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks
论文作者
论文摘要
联合学习（FL）是一种协作机器学习（ML）框架，它结合了设备培训和基于服务器的聚合，以在分布式代理之间培训常见的ML模型。在这项工作中，我们提出了一种异步的FL设计，并定期汇总，以解决FL系统中的Straggler问题。考虑到有限的无线通信资源，我们研究了不同的调度策略和聚合设计对收敛性能的影响。在减少汇总模型更新的偏见和差异的重要性的驱动下，我们提出了一项调度策略，共同考虑用户设备的渠道质量和培训数据表示。与同步FL提出的最新方法相比，我们的渠道感知数据的调度策略的有效性是通过模拟验证的。此外，我们表明``年龄了解''聚合加权设计可以显着改善异步FL设置的学习性能。
Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents. In this work, we propose an asynchronous FL design with periodic aggregation to tackle the straggler issue in FL systems. Considering limited wireless communication resources, we investigate the effect of different scheduling policies and aggregation designs on the convergence performance. Driven by the importance of reducing the bias and variance of the aggregated model updates, we propose a scheduling policy that jointly considers the channel quality and training data representation of user devices. The effectiveness of our channel-aware data-importance-based scheduling policy, compared with state-of-the-art methods proposed for synchronous FL, is validated through simulations. Moreover, we show that an ``age-aware'' aggregation weighting design can significantly improve the learning performance in an asynchronous FL setting.
