Federated learning (FL) has evolved as a prominent method for edge devices to cooperatively create a unified prediction model while securing their sensitive training data local to the device. Despite the existence of numerous research frameworks for simulating FL algorithms, they do not facilitate comprehensive deployment for automatic speech recognition tasks on heterogeneous edge devices. This is where Ed-Fed, a comprehensive and generic FL framework, comes in as a foundation for future practical FL system research. We also propose a novel resource-aware client selection algorithm to optimise the waiting time in the FL settings. We show that our approach can handle the straggler devices and dynamically set the training time for the selected devices in a round. Our evaluation has shown that the proposed approach significantly optimises waiting time in FL compared to conventional random client selection methods.

翻译：联邦学习（FL）已成为边缘设备在本地保护敏感训练数据的同时，协作构建统一预测模型的主流方法。尽管已有大量用于模拟FL算法的研究框架，但这些框架未能支持在异构边缘设备上对自动语音识别任务进行全面部署。为此，Ed-Fed作为一个全面且通用的FL框架应运而生，为未来实用的FL系统研究奠定基础。我们还提出了一种新颖的资源感知客户端选择算法，以优化FL环境中的等待时间。实验表明，与传统的随机客户端选择方法相比，我们的方法能有效处理落后设备，并动态设置每轮选中设备的训练时长。评估结果证明，所提方法显著优化了FL中的等待时间。