Work

Federated Learning for Soil Moisture Prediction: Benchmarking Lightweight CNNs and Robustness in Distributed Agricultural IoT Networks

Year: 2025

Type: article

Abstract: Federated learning (FL) provides a privacy-preserving approach for training machine learning models across distributed datasets; however, its deployment in environmental monitoring remains underexplored. This paper uses the WHIN dataset, comprising 144 weather stations across Indiana, to establish a benchmark for FL in soil moisture prediction. The work presents three primary contributions: the design of lightweight CNNs optimized for edge deployment, a comprehensive robustness assessment of FL under non-IID and adversarial conditions, and the development of a large-scale, reproducible agricultural FL benchmark using the WHIN network. The paper designs and evaluates lightweight (∼0.8 k parameters) and heavy (∼9.4 k parameters) convolutional neural networks (CNNs) under both centralized and federated settings, supported by ablation studies on feature importance and model architecture. Results show that lightweight CNNs achieve near-heavy CNN performance (MAE = 7.8 cbar vs. 7.6 cbar) while reducing computation and communication overhead. Beyond accuracy, this work systematically benchmarks robustness under adversarial and non-IID conditions, providing new insights for deploying federated models in agricultural IoT.

Source: Machine Learning and Knowledge Extraction

DOI: 10.3390/make7040132

Authors: Salma Zakzouk, Lobna A. Said

Institution: Nile University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $2,172