SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms

TL;DR

SusFL is an energy-efficient federated learning system designed for sustainable smart farms, enhancing animal health monitoring by optimizing energy use, ensuring system resilience, and improving prediction accuracy through innovative client selection and security measures.

Contribution

The paper introduces SusFL, a novel energy-aware federated learning framework with game-theoretic client selection and security features tailored for sustainable smart farming.

Findings

10% reduction in energy consumption

15% increase in social welfare

34% rise in MTBF

Abstract

We propose a novel energy-aware federated learning (FL)-based system, namely SusFL, for sustainable smart farming to address the challenge of inconsistent health monitoring due to fluctuating energy levels of solar sensors. This system equips animals, such as cattle, with solar sensors with computational capabilities, including Raspberry Pis, to train a local deep-learning model on health data. These sensors periodically update Long Range (LoRa) gateways, forming a wireless sensor network (WSN) to detect diseases like mastitis. Our proposed SusFL system incorporates mechanism design, a game theory concept, for intelligent client selection to optimize monitoring quality while minimizing energy use. This strategy ensures the system's sustainability and resilience against adversarial attacks, including data poisoning and privacy threats, that could disrupt FL operations. Through extensive comparative analysis using real-time datasets, we demonstrate that our FL-based monitoring system significantly outperforms existing methods in prediction accuracy, operational efficiency, system reliability (i.e., mean time between failures or MTBF), and social welfare maximization by the mechanism designer. Our findings validate the superiority of our system for effective and sustainable animal health monitoring in smart farms. The experimental results show that SusFL significantly improves system performance, including a $10\%$ reduction in energy consumption, a $15\%$ increase in social welfare, and a $34\%$ rise in Mean Time Between Failures (MTBF), alongside a marginal increase in the global model's prediction accuracy.