Abstract：
Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.
摘要：
尽管机器学习方法在空气质量评估方面取得了长足的进步，数据隐私方面的挑战依然存在，跨区域数据处理，和模型泛化。为了解决这些问题,我们引入了一种先进的联邦贝叶斯网络(FBN)方法。通过整合联邦学习，自适应优化算法，和同态加密技术，我们大大提高了跨区域空气质量数据处理的效率和安全性。这项研究的新颖之处在于对空气质量数据分析的联合学习进行了改进，特别是在分布式模型训练优化和数据一致性方面。通过自适应结构修改策略和模拟退火免疫优化算法的集成,我们显著提高了贝叶斯网络的结构学习精度，使预测精度提高了20%。此外，采用同态加密保证了数据传输的安全性和保密性。在我们的京津冀案例研究中，与传统方法相比，我们的方法在空气质量分类准确性方面提高了15％，并且在分析环境因素相互作用方面具有出色的可解释性。我们量化了各地区复杂的空气污染模式，发现空气质量指数的30％波动与NO2浓度相关。我们还观察到河北省和天津市的特定污染物指标与空气质量变化之间呈中等正相关。此外,在处理大规模和多源环境数据方面，FBN比其他机器学习模型表现出更好的操作效率和数据保密性。我们的FBN方法为环境监测和评估提供了新的视角，对于理解复杂的空气污染模式和制定未来的生态保护政策至关重要。
