Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting

FedTP: A Federated Learning Framework for Traffic Prediction

FedTT: A Federated Spatio-Temporal Learning Framework for Cross-City Traffic Knowledge Transfer

Traffic prediction aims to forecast future traffic conditions from historical spatio-temporal data. In practice, many cities face limited traffic data due to incomplete sensing infrastructures, making accurate prediction difficult. Federated traffic knowledge transfer offers a promising solution by allowing data-rich source cities to assist data-scarce target cities while keeping local data private. However, existing methods still face key challenges, including cross-city distribution discrepancies, incomplete or noisy observations, and potential privacy leakage from shared gradients or model parameters during federated optimization. To this end, we propose FedTT, a federated spatio-temporal learning framework for cross-city traffic knowledge transfer. FedTT features three co-designed components: (i) a Traffic Domain Adapter (TDA) that explicitly aligns heterogeneous traffic data distributions across cities to improve transfer effectiveness, (ii) a Traffic View Imputation (TVI) module that enhances data quality by completing missing traffic observations through spatio-temporal dependency modeling, and (iii) a lightweight Traffic Secret Aggregation (TSA) protocol that enables attack-resistant knowledge aggregation with formal privacy analysis, without relying on heavy cryptographic primitives. Extensive experiments on four real-world traffic datasets demonstrate that FedTT consistently outperforms 18 state-of-the-art baselines, achieving improved prediction accuracy while maintaining strong empirical resistance to attacks.

Benchmarking Data Heterogeneity Challenges in Federated Learning for Distributed Renewable Energy Forecasting: A Systematic Gap Analysis

Federated Learning (FL) offers a privacy-preserving framework for training energy forecasting models across geographically distributed nodes without centralising raw metering data. However, the accuracy of classical FL algorithms degrades substantially when client data is heterogeneous, a condition that is the norm rather than the exception in distributed energy systems. This paper presents a novel systematic benchmark study quantifying accuracy degradation across four distinct heterogeneity axes: quantity skew (Dirichlet $\alpha$), covariate shift, temporal non-stationarity, and client availability. Here, five FL algorithms (FedAvg, FedProx, SCAFFOLD, Local-Only, and Centralized independent and identically distributed (IID) oracle) are evaluated with long short-term memory (LSTM) and Neural Basis Expansion Analysis for Time Series (N-BEATS) architectures across public datasets (NREL Solar, NREL Wind, Pecan Street, UK Smart Meter), generating over 190 experimental conditions. This degradation gap analysis reveals that standard FedAvg suffers up to 83.4% Mean Absolute Error (MAE) degradation relative to the centralised IID baseline under heavy quantity skew on solar data, with temporal non-stationarity emerging as the dominant single-axis contributor.

Federated Learning Approach for Multi-Regional Traffic Flow Prediction

Experiments on controlled synthetic data and the Q-Traffic real-world dataset demonstrate that the proposed framework improves predictive accuracy, cross-client stability, and robustness under heterogeneous federated traffic scenarios.

An Investigation of Federated Learning for Air Quality Forecasting and Monitoring

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.

Meta-FedGeo: Adaptive Federated Learning with Spatiotemporal Transformers for Urban GeoAI in Smart Cities

This study introduces Meta-FedGeo, a federated learning framework that integrates meta-learning and spatiotemporal transformers to address key challenges in urban GeoAI for smart cities. Data streams in smart city environments are inherently non-stationary and heterogeneous, limiting the adaptability of traditional federated learning approaches. Meta-FedGeo overcomes these limitations through a hybrid centralised–decentralised architecture that pre-trains a global model using meta-learning to capture cross-city spatiotemporal patterns and dynamically refines it through federated updates. The framework incorporates performance-aware client selection and temporally weighted aggregation to enhance model robustness and convergence. To model complex urban dynamics, the proposed system employs a Spatio-Temporal Transformer (ST-Transformer). In addition, an Uncertainty-Calibrated Decision Engine (UCDE) is introduced to align model predictions with accessibility and urban planning constraints. Unlike static federated methods, Meta-FedGeo can dynamically identify and filter malicious or low-quality clients using local validation loss, while Shapley value-based mechanisms support efficient and fair knowledge transfer across distributed nodes. To clarify the scope of the present study, Meta-FedGeo is reported as a partially implemented research prototype: the ST-Transformer backbone, the meta-learning initialisation, the validation-loss-based client filtering and the temporal-weighted aggregation were implemented and evaluated on partitioned real-world datasets, whereas the Shapley-value contribution assessment, the Lightweight Data Harmonisers (LDHs) and the UCDE are presented as architectural components with proof-of-concept implementations whose full empirical validation is identified as future work. The framework is designed for seamless integration with existing urban infrastructure without requiring major modifications. Experimental results using real-world urban datasets partitioned into non-IID federated clients indicate improved predictive performance and faster convergence relative to the federated baselines considered here. Overall, Meta-FedGeo advances GeoAI toward scalable, adaptive, and practical applications in smart city environments.