The field of medical imaging currently faces the problems of data island and non-independent and independently distributed (Non-IID) variables in multi-center data. In this study, a federated learning algorithm based on adaptive aggregate weight (FedAaw) is proposed. Using a global model polymerization process, this study utilized the accuracy threshold to filter out the local model; the model accuracy is calculated by the center server. The corresponding weights of polymerization, which are updated in the global model, yielded models with better classification performances that are used to construct a global model, which helps address the problems associated with Non-IID multicenter data. Furthermore, to improve the applicability of the model to mining the information between the long and short distance of the image, the multi head self-attention mechanism is introduced to the local and global models. In addition, to address the problem of model overfitting caused by end-to-end redundant features, the convolution kernel features in the global model are extracted. The learning of sparse Bayesian extreme learning machine based on L₁ norm (SBELML₁) framework is used for the feature classification of the data obtained from each center. Finally, the anti-interference ability of the FedAaw algorithm is verified by shuffling the data distribution of different centers several times. The AUC ranges of the test sets used in the five centers are as follows: center 1: (0.7947?0.8037), center 2: (0.8105?0.8405), center 3: (0.6768?0.7758), center 4: (0.8496?0.9063), and center 5: (0.8913?0.9348). These results indicate that FedAaw has good classification performance on multi-center data and a strong anti-interference ability.