In recent years, the method of transforming public safety data into graph form and constructing node representations through graph neural networks for training and inference of downstream tasks has fully exploited the entity and association information of public safety data, achieving excellent results. Nevertheless, to enhance the effectiveness of the model, a large amount of high-quality data is needed, which is usually held by governments, companies, and organizations, making it difficult to learn an effective event detection model through data centralization. Moreover, due to different focuses and collection times of the data from various parties, there is a Non-IID (independent and identically distributed) problem among the data. Traditional methods that assume a global model can accommodate all clients are challenging to solve such issues. Therefore, this study proposes personalized public safety event detection (PPSED) method based on a reinforcement federated graph neural network. In this method, each client trains a personalized and more robust model through multi-party collaboration to solve local event detection tasks. A local training and gradient quantization module is designed for the federated public safety emergency event detection model and trained GraphSage through a minibatch mechanism based on graph sampling to construct a local model for public safety event detection. This approach reduces the impact of Non-IID data and supports the gradient quantization method to lower the consumption of gradient communication. A client state awareness module is also designed based on random graph embedding, which better retains the valuable information of the client model while protecting privacy. Furthermore, a personalized gradient aggregation and quantization strategy are designed for the federated graph neural network. Deep deterministic policy gradient (DDPG) is used to fit a personalized federated learning gradient aggregation weighting strategy, and it is determined whether the gradient can be quantized based on the weight, balancing the model's performance, and communication pressure. This study demonstrated the effectiveness of the method through extensive experiments on a public safety dataset collected from the Weibo platform and three public graph datasets.