Privacy-Preserving Spatial–Temporal Traffic Analytics through Federated Large Language Models for Smart Cities
Keywords:
Federated learning; large language models; smart cities; traffic analytics; privacy preservation; spatial-temporal data; intelligent transportation systemsAbstract
The pervasive deployment of sensing infrastructure in smart cities generates vast streams of spatial-temporal traffic data that underpin real-time mobility services and urban planning. Centralizing these data for analytics, however, creates severe privacy vulnerabilities and regulatory tensions. Federated learning offers a decentralized training paradigm that keeps raw data on edge devices, while large language models have recently demonstrated remarkable capabilities for sequence modeling and reasoning that can be adapted to traffic forecasting. The intersection of these two transformative technologies remains largely unexplored. This paper presents a system-level analysis of a privacy-preserving federated large language model architecture designed for smart-city traffic analytics, examining structural trade-offs, deployment challenges, and governance frameworks. We propose a split-learning and federated fine-tuning framework in which a pre-trained language backbone is augmented with lightweight adapters that capture spatial-temporal dependencies, trained collaboratively across heterogeneous edge nodes under differential privacy and secure aggregation. The discussion extends to communication-computation balancing, model heterogeneity, fairness across urban jurisdictions, and long-term sustainability. By integrating perspectives from distributed systems, intelligent transportation, and responsible artificial intelligence, the paper outlines a roadmap for enabling high-capacity, privacy-respecting traffic intelligence in connected urban environments.
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