M²FedAQI is a lightweight multimodal federated framework that fuses visual and tabular data via feature modulation for improved AQI prediction and regression on heterogeneous edge devices.
Multimodal federated learning for air quality estimation with model personalization over aerial-ground networks.IEEE Geoscience and Remote Sensing Letters
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
M$^2$FedAQI: Multimodal Federated Learning for Air Quality Prediction on Heterogeneous Edge Devices
M²FedAQI is a lightweight multimodal federated framework that fuses visual and tabular data via feature modulation for improved AQI prediction and regression on heterogeneous edge devices.