Federated Learning Over LoRa Networks: Simulator Design and Performance Evaluation

2025-08-14

AI Summary

To address the challenges of bandwidth constraints, severe interference, and duty-cycle limitations hindering federated learning (FL) deployment in LoRa-based low-power wide-area networks (LPWANs), this work designs and implements a lightweight FL simulation platform integrating Flower and LoRaSim. We propose a fine-grained, link-level LoRa channel model that accurately captures receiver sensitivity, multi-source interference, and block fading effects. Systematic evaluation is conducted on the impact of forward error correction (FEC), update sparsification, quantization compression, and duty cycling on FL convergence behavior and over-the-air transmission time. Experimental results demonstrate that judicious FEC configuration significantly improves both convergence speed and stability while reducing device communication overhead. This work provides a reproducible simulation framework and key design guidelines for efficient FL protocol development in resource-constrained LPWAN environments.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Federated learning (FL) over long-range (LoRa) low-power wide area networks faces unique challenges due to limited bandwidth, interference, and strict duty-cycle constraints. We develop a Python-based simulator that integrates and extends the Flower and LoRaSim frameworks to evaluate centralized FL over LoRa networks. The simulator employs a detailed link-level model for FL update transfer over LoRa channels, capturing LoRa's receiver sensitivity, interference characteristics, block-fading effects, and constraints on the maximum transmission unit. It supports update sparsification, quantization, compression, forward frame-erasure correction (FEC), and duty cycling. Numerical results illustrate the impact of transmission parameters (spreading factor, FEC rate) and interference on FL performance. Demonstrating the critical role of FEC in enabling FL over LoRa networks, we perform an in-depth evaluation of the impact of FEC on FL convergence and device airtime, providing insights for communication protocol design for FL over LoRa networks.

Problem

• Evaluating FL performance over bandwidth-limited LoRa networks
• Designing a simulator for FL with LoRa-specific constraints
• Analyzing FEC impact on FL convergence and airtime

Innovation

• Python-based simulator integrates Flower and LoRaSim
• Detailed link-level model for FL over LoRa
• Evaluates FEC impact on FL convergence