susFL: Federated Learning-based Monitoring for Sustainable, Attack-Resistant Smart Farms
Dian Chen, Paul Yang, Dong Sam Ha, Jin-Hee Cho · 2024
We propose a sustainable federated learning (FL)-based monitoring system, namely susFL, for smart animal farms 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 a game-theoretic approach, called mechanism design, to select intelligent clients to optimize monitoring quality while minimizing energy use. This strategy ensures the system’s sustainability and resilience against various adversarial attacks, including data poisoning and privacy threats, that could disrupt FL operations. Our work in smart farm technologies sets a new standard by developing an animal monitoring system that is both energy-adaptive and resistant to attacks. Through extensive experiments, 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 experimental results show that susFL significantly outperforms the state-of-the-art counterparts, including a 10% reduction in energy consumption, a 15% increase in social welfare, and a 34% rise in Mean Time Between Failures (MTBF) while maintaining the global model’s prediction accuracy.