Citymind: A Multimodal Federated Transformer Framework for Real-Time Urban Anomaly Detection and Adaptive City Response

A tremendous amount of data is constantly being generated by sources such as traffic and weather cameras, environmental sensors, digital infrastructure in city areas. In this situation, it has become a big challenge for the smart city managers to detect unusual events in real-time. In this paper, we have proposed a machine learning system based on the METR-LA city traffic dataset that detects abnormal conditions related to traffic and mobility. The proposed framework involves understanding the relationships between road segments by integrating temporal modeling with graph-based spatial learning and works accordingly to the dynamic changes that take place in the traffic flow. Using LSTM and transformer-based sequence modeling techniques, the system predicts normal traffic behavior and identifies deviations from that normal state - such as those associated with traffic congestion, accidents, or infrastructure faults. Experimental results show that, compared to traditional threshold-based and isolation-based methods, the proposed approach achieves greater accuracy, faster convergence, and lower false alarm rate. These results demonstrate that this approach can be effective in providing intelligent decision support to smart city traffic control systems.