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

2026-04-12

AI Summary

This work addresses the degraded performance of federated learning in large-scale IoT edge networks, where resource-constrained nodes and small-scale heterogeneous data pose significant challenges. To tackle this issue, the authors propose an energy-efficient co-optimization framework that jointly models expected learning loss and resource allocation for the first time. A stochastic online learning algorithm with convergence-bound constraints is designed to adapt to dynamic data distributions while ensuring theoretical convergence guarantees. The resulting scalable online distributed algorithm substantially enhances learning efficiency and energy utilization, particularly in small-data regimes. Extensive simulations and real-world autonomous driving obstacle-avoidance experiments demonstrate that the proposed approach consistently outperforms existing methods in both model performance and energy efficiency.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Federated Edge Learning
• Small-Scale Datasets
• Energy Efficiency
• IoT Networks
• Resource Constraints

Innovation

• federated edge learning
• energy efficiency
• small-scale datasets
• online distributed algorithm
• resource optimization