Aug 2026· Mathematical Modeling and Algorithm Application· 0 citations· 11 references
TL;DR
There are significant gaps that remain in terms of model interpretability and the ability to generalize across climate variations, so this article provides a relatively comprehensive overview of the application of federated learning in air quality forecasting and monitoring.
Abstract
Air quality, as a global issue, impacts people’s health and daily life. It requires precise prediction and monitoring for sustainable urban management. Conventional centralized air quality prediction methods are limited by data privacy, high communication costs, and low scalability in distributed environments. Federated learning offers a solution that collaboratively trains global models without sharing raw data. The passage conducted a comprehensive literature analysis to categorize FL applications in air quality research into four technical streams: Probabilistic Graphical Models (Federated Bayesian Networks) for causal inference; Time-series Deep Learning (integrating LSTM/CNN with FedAvg/FedProx) for temporal pattern extraction; Spatio-temporal Graph Neural Networks (GC-LSTM) for capturing complex spatial dependencies; and Multi-model Ensemble with Transfer Learning for heterogeneous client adaptation. Nevertheless, there are significant gaps that remain in terms of model interpretability and the ability to generalize across climate variations. All in all, this article provides a relatively comprehensive overview of the application of federated learning in air quality forecasting and monitoring. It will assist researchers in advancing this field in the future.
Federated learning has become a popular model to apply in privacy-preserving modeling in distributed settings, particularly when the models are applied to data that is distributed among various locations and is often sensitive, such as in air quality prediction. This paper looks into how effective federated learning is for predicting ozone (O₃) concentrations, under both independent and non-independent data distributions. In particular, two representative algorithms Federated Averaging (FedAvg)and Federated Averaging (FedAvg) are experimented on a real-world air quality dataset. An experimental framework was established that was relatively comprehensive and centralized training and local-only models were used as baselines. Mean absolute error (MAE) and root mean squared error (RMSE) are used to measure model performance. The findings suggest that federated learning performs much better than the isolated local models and the performance is similar to that of the centralized training. Having said that, data heterogeneity does present certain issues-it slows down convergence and decreases accuracy in prediction. In such non-IID conditions, FedProx is more stable and less erroneous than FedAvg implying that it is more resistant to client drift.Most importantly, this paper provides empirical data on the impact of data heterogeneity on federated learning, and proves that federated learning can be a viable alternative in privacy-sensitive environmental prediction problems.
Zheyu Qiu· Mathematical Modeling and Al...· 0 citations
Ground ozone and other air pollutants pose a great threat to the health. Because the data of different monitoring stations are scattered, it is very complicated to accurately predict the ozone level. Federated Learning (FL) allows everyone to train models together without exchanging raw data, thus protecting privacy. This paper uses FL to predict air quality, and the key point is to establish a prediction model, not to change the algorithm. This paper used UCI air quality data set (with 8,762 records, each with four attributes) to simulate the situation of five users, and the data were separated by independent identically distributed (IID) and non-independent identically distributed (Non-IID). This paper trained 50 communication rounds with a multi-layer perceptron (MLP) and Federal Average (FedAvg) method. The model of centralized training and local training is also used to compare. The experimental results show that the average absolute error (MAE) of FedAvg is 48.9 under IID data and 58.3 under Non-IID data, which is much better than the local training models (MAE 78.5 and 85.2) and close to the effect of centralized training (MAE 42.3 and 51.6). This method of federated learning adapts well to different data. This study confirms that FL is a feasible method for distributed air quality prediction.
Jiaxuan Xu· Mathematical Modeling and Al...· 0 citations
The findings highlight the potential of HFL with fog computing as a scalable, cost-effective, and privacy-preserving solution for real-time urban air quality monitoring in India, particularly in resource-constrained smart city environments.
Satya Krishna, Nunna, A. Manju et al.· SN Computer Science· 0 citations
Accurate air pollution forecasting is vital for protecting the environment and public health. However, predicting air pollution continues to be a challenge due to complicated relationships between meteorological variables, pollutant concentrations, and time-dependent characteristics. This paper proposes a Temporal-Aware Multi-Task Learning (TMTL-AQI) framework to assess urban air quality via structured data. The model simultaneously performs three tasks: Air Quality Index category classification, PM2.5 and PM10 particulate matter regression, and auxiliary AQI value prediction. The framework consists of data preprocessing, cyclical temporal encoding, a shared backbone based on hard parameter sharing, and three task-specific output heads optimized jointly. It uses cyclical transformations to encode temporal traits that capture periodic patterns of the environment and produces a common neural representation through hard parameter sharing. The TRAQID dataset was used for empirical testing. The model outperforms single-task and baseline multi-task models with an accuracy of 0.7605 and an F1-score of 0.7422 for AQI classification. Although based solely on structured input (no image-based features), the model performed competitively with a state-of-the-art approach due to the reduced prediction error in regression tasks (MAE values of 13.34 μg/m³ for PM2.5 and 22.06 μg/m³ for PM10).
Iman Youssif Ibrahim, D. M. Ahmed· Dasinya Journal for Engineer...· 0 citations
An optimized machine learning-based Air Quality Forecasting System that integrates Extreme Learning Machines (ELM) and Genetic Algorithms (GA) to predict short-term variations in air quality and demonstrates a robust, scalable, and practical solution for short-term air quality prediction.
Shivatejaswini B, D. B M, Mr. Gattu Prasad· International Scientific Jou...· 0 citations
