A Federated Learning Framework for Brain Tumor Segmentation Without Sharing Patient Data

Brain tumors pose a significant threat to human health, necessitating early detection and accurate diagnosis to enhance treatment outcomes. However, centralized data collection and processing encounter challenges related to privacy breaches and data integration due to the sensitivity and diversity of brain tumor patient data. In response, this paper proposes an innovative federated learning-based approach for brain tumor detection, facilitating multicenter data sharing while safeguarding individual data privacy. Our proposed federated learning architecture features each medical center as a participant, with each retaining local data and engaging in secure communication with a central server. Within this federated migration learning framework, each medical center independently trains a base model on its local data and transmits a fraction of the model's parameters to the central server. The central server leverages these parameters for model aggregation and knowledge sharing, facilitating the exchange and migration of models among participating medical centers. This collaborative approach empowers individual medical centers to share knowledge and experiences, thereby enhancing the performance and accuracy of the brain tumor detection model. To validate our federated learning model, we conduct comprehensive evaluations using an independent test dataset, comparing its performance with traditional centralized learning approaches. The experimental results underscore the superiority of the federated learning-based brain tumor detection approach, achieving heightened detection performance compared with traditional methods while meticulously preserving data privacy. In conclusion, our study presents an innovative solution for effective data collaboration and privacy protection in the realm of brain tumor detection, holding promising clinical applications. The federated learning approach not only advances detection accuracy but also establishes a secure and privacy-preserving foundation for collaborative research in medical imaging.