Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation
This work addresses data privacy issues in medical imaging for clinicians and researchers, but it is incremental as it focuses on optimizing existing federated learning methods.
The paper tackles the challenge of training effective AI models for brain tumor segmentation without centralizing sensitive medical data by evaluating various federated learning approaches, achieving competitive performance with faster convergence and robustness to non-IID data distributions.
Availability of large, diverse, and multi-national datasets is crucial for the development of effective and clinically applicable AI systems in the medical imaging domain. However, forming a global model by bringing these datasets together at a central location, comes along with various data privacy and ownership problems. To alleviate these problems, several recent studies focus on the federated learning paradigm, a distributed learning approach for decentralized data. Federated learning leverages all the available data without any need for sharing collaborators' data with each other or collecting them on a central server. Studies show that federated learning can provide competitive performance with conventional central training, while having a good generalization capability. In this work, we have investigated several federated learning approaches on the brain tumor segmentation problem. We explore different strategies for faster convergence and better performance which can also work on strong Non-IID cases.