•The study proposes a layered sharing architecture based on blockchain and federated learning, enhancing the security, accuracy, and efficiency of data sharing on Internet of Things devices.•The architecture enables model updates and security verification through client node clustering and blockchain consensus processes, ensuring reliable and secure data transmission.•Device clustering federated learning algorithm: A device clustering federated learning algorithm, based on label similarity, is designed to address the issue of imbalanced distribution of data labels. This algorithm improves the accuracy and stability of the system model.•High accuracy and low communication cost: Experimental results demonstrate that the research algorithm achieves 95 % accuracy after 30 iterations, with a relatively low communication cost. The system stability is also observed to increase with the number of label categories.•Improved efficiency in medical sharing system: In the context of a certain hospital's medical sharing system, the research system outperforms the original system by extracting information in 42.9 % less time while maintaining an accuracy of over 98 %. This highlights the effectiveness of the research methods in improving data transmission efficiency and accuracy in Internet of Things data sharing systems, with potential applications in other fields. To share data on Internet of Things devices more securely, accurately, and efficiently, this study designs a layered sharing architecture based on blockchain and federated learning. This architecture achieves efficient and secure Internet of Things data sharing through client node clustering and blockchain consensus processes. In addition, to address the issue of imbalanced distribution of data labels in system devices, a device clustering federated learning algorithm based on label similarity is designed to improve the accuracy and stability of the model. The experimental results showed that under independent synchronous data distribution and non independent synchronous data distribution, the research algorithm achieved a 95 % accuracy after 30 iterations, and the communication cost was relatively low. When testing algorithm stability under non independent synchronous data distribution, the more label categories there are, the higher the accuracy. When the label category M = 12, the accuracy could reach 96.0 %. In the medical sharing system of a certain hospital, the research system took about 42.9 % less time to extract information than the original system, and the accuracy could be maintained at over 98 %. This research method can effectively solve the problem of uneven distribution of device data labels, and improve the data transmission efficiency and accuracy of Internet of Things data sharing systems. Moreover, this method can also reduce the impact of malicious nodes on the global model, providing technical support for data transmission and security protection in other fields.