SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable
  Smart Farms
Dian Chen Paul Yang Ing-Ray Chen Dong Sam Ha Jin-Hee Cho
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.