Data-Quality Based Scheduling for Federated Edge Learning

TL;DR

This paper introduces a data-quality based scheduling algorithm for federated edge learning that improves device selection and bandwidth allocation, enhancing training convergence amid data heterogeneity and communication constraints.

Contribution

It proposes a novel DQS algorithm that prioritizes reliable devices with diverse data, addressing data heterogeneity and communication challenges in FEEL.

Findings

DQS improves convergence in heterogeneous data scenarios.

DQS outperforms baseline scheduling methods.

Effective in data poisoning scenarios.

Abstract

FEderated Edge Learning (FEEL) has emerged as a leading technique for privacy-preserving distributed training in wireless edge networks, where edge devices collaboratively train machine learning (ML) models with the orchestration of a server. However, due to frequent communication, FEEL needs to be adapted to the limited communication bandwidth. Furthermore, the statistical heterogeneity of local datasets' distributions, and the uncertainty about the data quality pose important challenges to the training's convergence. Therefore, a meticulous selection of the participating devices and an analogous bandwidth allocation are necessary. In this paper, we propose a data-quality based scheduling (DQS) algorithm for FEEL. DQS prioritizes reliable devices with rich and diverse datasets. In this paper, we define the different components of the learning algorithm and the data-quality evaluation. Then, we formulate the device selection and the bandwidth allocation problem. Finally, we present our DQS algorithm for FEEL, and we evaluate it in different data poisoning scenarios.