AI RESEARCH
FedLLM: A Privacy-Preserving Federated Large Language Model for Explainable Traffic Flow Prediction
arXiv CS.LG
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ArXi:2604.16612v1 Announce Type: new Traffic prediction plays a central role in intelligent transportation systems (ITS) by ing real-time decision-making, congestion management, and long-term planning. However, many existing approaches face practical limitations. Most spatio-temporal models are trained on centralized data, rely on numerical representations, and offer limited explainability.