Federated Transfer Learning for Medical Image Classification under Mixed Skew

This paper addresses federated transfer learning for medical image classification when data distributions differ across institutions (mixed skew), a common challenge in healthcare AI deployments. The research proposes methods to improve model performance when organizations cannot share raw patient data due to privacy constraints, enabling collaborative AI development across healthcare systems while maintaining data governance compliance.

This paper addresses federated transfer learning for medical image classification when data distributions differ across institutions (mixed skew), a common challenge in healthcare AI deployments. The research proposes methods to improve model performance when organizations cannot share raw patient data due to privacy constraints, enabling collaborative AI development across healthcare systems while maintaining data governance compliance.

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