Spatial-temporal prediction plays a crucial role in various domains, including intelligent transportation and environmental monitoring. Although large language model has shown advantages in long-range dependency modeling and excellent generalization ability for forecasting, it has limited understanding of spatial-temporal features. Especially for spatial features, most existing methods still simplify the spatial-temporal prediction task into multiple independent temporal prediction tasks, failing to effectively encode the dynamic evolution of spatial relations. To address these problems, we propose ST-VLM (Spatial-Temporal Forecasting with Vision-Language Model), a novel framework that leverages visual representations to encode the dynamic spatial dependencies within spatial-temporal data and integrates multi-modal information to enhance prediction. This framework transforms spatial-temporal features into three modalities: vision, text, and time series, enhances cross-modal fusion through an attention-aware fusion mechanism in the first-layer of Vision-Language Model (VLM), optimizes multi-modal feature interaction via adaptive fine-tuning strategies. After fusion, the multi-modal embeddings are subsequently used for the final spatial-temporal prediction task. Extensive experiments demonstrate that ST-VLM achieves state-of-the-art performance across various datasets. In particular, the framework exhibits promising results in few-shot scenarios, verifying its strong generalization ability.

Personalized federated graph neural networks (PFGNN) are an emerging technology that allows multiple graph data owners to collaboratively train personalized models without sharing raw data. However, the Non-IID nature of graph data can cause the coupling of global and local knowledge parameters, which disrupts the optimization in personalized federated learning. Additionally, node neighbors may carry global and local knowledge, and their direct inclusion in training may introduce noise, degrading federated model performance. In this work, we propose the Adaptive Decoupling Personalized Federated Graph Neural Network (ADPFedGNN), which leverages multi-party collaboration to train personalized models for classifying local client graph nodes. We use two automatically updated masks and mutual information minimization to decouple global and local parameters in FGNN. We employ reinforcement learning to adaptively select appropriate neighbors for training global or local knowledge-related parameters while filtering out irrelevant nodes. We also design a personalized federated masked parameter aggregation mechanism that efficiently updates local personalized model parameters and aggregates the masked parameters. Experimental results on three public datasets demonstrate that ADPFedGNN outperforms existing methods, achieving average improvements of 5.66 percent, 5.83 percent, and 12.45 percent in ACC, F1, and Recall, respectively.