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.