Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices

TL;DR

Ed-Fed is a versatile federated learning framework designed for edge devices, featuring a resource-aware client selection algorithm that reduces waiting times and handles device heterogeneity effectively.

Contribution

This paper introduces Ed-Fed, a comprehensive FL framework tailored for speech recognition on heterogeneous edge devices, with a novel resource-aware client selection method.

Findings

Significantly reduces waiting time compared to random selection.

Effectively manages straggler devices during training rounds.

Dynamically adjusts training time for selected clients.

Abstract

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.