With the continuous advancement of artificial intelligence technologies in medical imaging, there is an increasing demand for the collaborative utilization of imaging data across multiple institutions. However, factors such as decentralized data storage, privacy protection constraints, and heterogeneous imaging distributions have become significant barriers to achieving optimal model training performance. To address challenges—including the difficulty of centralized sharing of medical imaging data and the limited adaptability of deep learning models across diverse scenarios—the present work proposes a federated learning-based application methodology designed with privacy and security in mind. This methodology leverages a distributed collaborative training framework and integrates key technologies—including model parameter aggregation, secure privacy-preserving computation, and heterogeneous imaging adaptation—to optimize the model training pipeline for medical imaging tasks such as classification, object detection, and image segmentation. Model performance is evaluated using publicly available medical imaging datasets, and the effectiveness of various federated learning strategies is compared in terms of recognition accuracy, model stability, and privacy protection capabilities. The results demonstrate that the federated learning approach not only reduces the need for exchanging raw imaging data but also maintains robust model recognition performance while enhancing the model’s adaptability in multi-source, heterogeneous imaging environments, thereby providing a viable technical solution for secure collaborative model training in intelligent medical imaging analysis tasks.