With the increasing emphasis on data security in public safety emergencies， federated learning has gained attention for its ability to perform computations without uploading data to a central server， thereby reducing the risk of privacy breaches. However， current federated learning approaches based on smart contracts face challenges such as inefficiency due to their computational demands. To address it， this paper proposes an asynchronous federated learning method for detecting public health emergencies， integrating smart contracts and federated storage. This approach allows federated nodes to join and leave the federated learning process at any time. By leveraging smart contracts and distributed storage， it enhances data security and training efficiency in the public health domain. Furthermore， adaptive differential privacy is employed to dynamically protect the gradients uploaded to distributed storage nodes， further reducing the risk of privacy leakage. Extensive experiments conducted on public datasets and public health security datasets demonstrate that the proposed method outperforms existing approaches in terms of accuracy and requires less time to achieve the same level of precision.