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.

我们提出了一种新颖的节能联邦学习系统，名为 SusFL，用于可持续智能农业，以解决由于太阳能传感器能量波动导致的不一致健康监测的挑战。该系统装备了搭载树莓派等计算能力的太阳能传感器，用于训练本地深度学习模型处理健康数据。这些传感器定期更新 Long Range (LoRa) 网关，构建无线传感器网络 (WSN) 以检测乳腺炎等疾病。我们提出的 SusFL 系统将机制设计，一个博弈论概念，用于智能客户端选择以优化监测质量并最小化能量消耗。通过广泛的实时数据集比较分析，我们证明了我们基于联邦学习的监测系统在预测准确性、操作效率、系统可靠性（即平均故障时间 MTBF）和机制设计者的社会福利最大化方面明显优于现有方法。我们的发现验证了我们的系统在智能农场中进行有效和可持续的动物健康监测的优越性。实验结果表明，SusFL 在系统性能方面显著改进，包括能耗减少 10％，社会福利增加 15％，故障平均时间 MTBF 上升 34％，全局模型的预测准确性略微增加。