Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation. (arXiv:2202.08261v1 [cs.LG])

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.

Source: https://arxiv.org/abs/2202.08261

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