Privacy-preserving blockchain-based federated learning for brain tumor segmentation

Improved data sharing between healthcare providers can lead to a higher probability of accurate diagnosis, more effective treatments, and enhanced capabilities of healthcare organizations. One critical area of focus is brain tumor segmentation, a complex task due to the heterogeneous appearance, irregular shape, and variable location of tumors. Accurate segmentation is essential for proper diagnosis and effective treatment planning, yet current techniques often fall short due to these complexities. However, the sensitive nature of health data often prohibits its sharing. Moreover, the healthcare industry faces significant issues, including preserving the privacy of the model and instilling trust in the model. This paper proposes a framework to address these privacy and trust issues by introducing a mechanism for training the global model using federated learning and sharing the encrypted learned parameters via a permissioned blockchain. The blockchain-federated learning algorithm we designed aggregates gradients in the permissioned blockchain to decentralize the global model, while the introduced masking approach retains the privacy of the model parameters. Unlike traditional raw data sharing, this approach enables hospitals or medical research centers to contribute to a globally learned model, thereby enhancing the performance of the central model for all participating medical entities. As a result, the global model can learn about several specific diseases and benefit each contributor with new disease diagnosis tasks, leading to improved treatment options. The proposed algorithm ensures the quality of model data when aggregating the local model, using an asynchronous federated learning procedure to evaluate the shared model's quality. The experimental results demonstrate the efficacy of the proposed scheme for the critical and challenging task of brain tumor segmentation. Specifically, our method achieved a 1.99% improvement in Dice similarity coefficient for enhancing tumors and a 19.08% reduction in Hausdorff distance for whole tumors compared to the baseline methods, highlighting the significant advancement in segmentation performance and reliability.