用多模态联邦学习预测空气质量，兼顾隐私与效率
M$^2$FedAQI: Multimodal Federated Learning for Air Quality Prediction on Heterogeneous Edge Devices
- 融合图像与表格数据，通过轻量级机制实现跨模态高效交互
- 在多个数据集上提升精度，最高比基线高11%准确率
- 适合边缘设备部署，通信开销小且支持安全认证
精准的空气质量预测对公共健康、环境监测和工业安全至关重要。然而，现有方法大多依赖集中式学习，带来可扩展性差、隐私泄露和通信开销大的问题。当前基于联邦学习（FL）的方案多使用单一模态数据，难以捕捉复杂环境模式。为此，我们提出M$^2$FedAQI，一种面向异构边缘设备的轻量化多模态联邦学习框架，用于去中心化空气污染指数（AQI）预测。该框架通过基于特征调制的融合机制整合视觉与表格模态数据，实现高效跨模态交互并保持低计算开销。在PM25Vision和TRAQID两个基准数据集上，针对分类与回归任务，在集中式与联邦设置下进行评估。实验结果表明，M$^2$FedAQI持续优于现有方法，准确率最高提升11.0%，AUC提升3.53%，F1分数提升12.2%，R²提升18.0%，同时平均绝对误差（MAE）和均方根误差（RMSE）分别降低25.4%和20.4%。此外，在异构边缘设备上的部署验证了其在通信开销、内存占用和计算成本方面的高效资源利用。为增强通信安全性，引入基于TLS的身份认证，保障客户端参与与通信通道安全，无需修改底层联邦学习协议。
原文摘要 · Abstract (English)
Accurate air quality prediction is essential for public health, environmental monitoring, and industrial safety. However, most existing approaches rely on centralized learning paradigms, which introduce challenges related to scalability, privacy preservation, and communication overhead in distributed Internet of Things (IoT) environments. Moreover, current federated learning (FL) based solutions predominantly utilize unimodal data, limiting their capability to capture complex environmental patterns. To address these limitations, we propose M$^2$FedAQI, a lightweight multimodal federated framework for decentralized Air Quality Index (AQI) prediction across heterogeneous edge devices. The proposed framework integrates visual and tabular modalities through a feature modulation based fusion mechanism that enables efficient cross-modal interaction while maintaining low computational overhead. M$^2$FedAQI is evaluated on two benchmark datasets, PM25Vision and TRAQID, for both classification and regression tasks under centralized and federated settings. Experimental results demonstrate that M$^2$FedAQI consistently outperforms existing approaches, achieving improvements of up to 11.0\% in Accuracy, 3.53\% in AUC, 12.2\% in F1-score, and 18.0\% in $R^2$, while reducing MAE and RMSE by up to 25.4\% and 20.4\%, respectively, compared with the strongest baselines. Furthermore, deployment on heterogeneous edge devices demonstrates efficient resource utilization in terms of communication overhead, memory footprint, and computational cost. To enhance communication security, TLS-based authentication is incorporated to ensure secure client participation and protect the FL communication channel from unauthorized third-party access without modifying the underlying FL protocol.
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