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.

Ed-Fed 是一个全面通用的联邦学习框架，提出了一种新颖的资源感知客户端选择算法来优化 FL 中的等待时间，相比于传统随机选择方法，本方法大大优化了 FL 中的等待时间。