Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices

2025-09-16

AI Summary

To address the challenges of limited energy budgets, unreliable wireless channels, and short-packet transmission constraints in resource-constrained IoT devices participating in federated learning (FL), this paper proposes a high-energy-efficiency FL framework. The framework jointly optimizes uplink transmit power while integrating finite-blocklength communication modeling, lightweight model quantization, and error-aware aggregation—thereby jointly enhancing communication reliability, energy efficiency, and model accuracy. Experimental results under typical IoT settings demonstrate that the proposed method reduces system energy consumption by up to 75% compared to standard FedAvg, with a bounded degradation in model accuracy (<1.2%). This substantial improvement in energy efficiency—without compromising learning performance—significantly enhances the practicality and deployability of edge-coordinated FL training in real-world IoT deployments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while preserving data privacy, making it particularly suitable for Internet of Things (IoT) environments. However, resource-constrained IoT devices face significant challenges due to limited energy,unreliable communication channels, and the impracticality of assuming infinite blocklength transmission. This paper proposes a federated learning framework for IoT networks that integrates finite blocklength transmission, model quantization, and an error-aware aggregation mechanism to enhance energy efficiency and communication reliability. The framework also optimizes uplink transmission power to balance energy savings and model performance. Simulation results demonstrate that the proposed approach significantly reduces energy consumption by up to 75% compared to a standard FL model, while maintaining robust model accuracy, making it a viable solution for FL in real-world IoT scenarios with constrained resources. This work paves the way for efficient and reliable FL implementations in practical IoT deployments. Index Terms: Federated learning, IoT, finite blocklength, quantization, energy efficiency.

Problem

• Optimizing energy-efficient federated learning for resource-constrained IoT devices
• Addressing unreliable communication with finite blocklength transmission
• Balancing model quantization with accuracy preservation in FL

Innovation

• Finite blocklength transmission integration
• Model quantization for efficiency
• Error-aware aggregation mechanism enhancement