Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks

TL;DR

This paper introduces an energy-efficient federated edge learning framework tailored for small-scale IoT datasets, optimizing resource use and enhancing learning performance in resource-constrained environments.

Contribution

It presents a novel collaborative optimization framework with a stochastic online learning algorithm and scalable distributed optimization for IoT edge networks.

Findings

Significant improvement in learning performance over benchmarks.

Enhanced resource efficiency in IoT edge learning.

Effective handling of small-scale datasets in large networks.

Abstract

Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constraints. Collecting heterogeneous sensory data, especially in small-scale datasets, is challenging, and independent edge nodes can lead to inefficient resource utilization and reduced learning performance. To address these issues, this paper proposes a collaborative optimization framework for energy-efficient federated edge learning with small-scale datasets. We first derive an expected learning loss to quantify the relationship between the number of training samples and learning objectives. A stochastic online learning algorithm is then designed to adapt to data variations, and a resource optimization problem with a convergence bound is formulated. Finally, an online distributed algorithm efficiently solves large-scale optimization problems with high scalability. Extensive simulations and autonomous navigation case studies with collision avoidance demonstrate that the proposed approach significantly improves learning performance and resource efficiency compared to state-of-the-art benchmarks.