Clustered Federated Learning Based on Mahalanobis Distance for Sequential Medical Data
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초록
In hospitals, metadata typically contains patients' personal information based on the doctor's diagnosis. Therefore, sniffers or hijackers could launch attacks to steal important information from hospitals or patients. For this reason, hospital data must be anonymized and protected by specialized systems to ensure its safe use, especially when multiple hospitals share data. If hospitals implement systems that can securely share data while maintaining privacy, researchers and clinicians can leverage large amounts of distributed data to more effectively train deep learning models. In this context, we select a solution based on clustered federated learning (CFL). In typical CFL scenarios, forming appropriate clusters can help build more personalized models for different groups. However, previous CFL approaches still face challenges from model heterogeneity. To further mitigate the heterogeneity problem, we propose a Mahalanobis distance based clustered federated learning (MD-CFL) method, which offers advantages in reducing model heterogeneity and improving clustering performance by correcting for feature skew in non-normalized data. Our experiments show that MD-CFL achieves accurate clustering performance, with a higher silhouette score compared to cosine-based FedAvg.
Abstract
In hospitals, metadata typically contains patients' personal information based on the doctor's diagnosis. Therefore, sniffers or hijackers could launch attacks to steal important information from hospitals or patients. For this reason, hospital data must be anonymized and protected by specialized systems to ensure its safe use, especially when multiple hospitals share data. If hospitals implement systems that can securely share data while maintaining privacy, researchers and clinicians can leverage large amounts of distributed data to more effectively train deep learning models. In this context, we select a solution based on clustered federated learning (CFL). In typical CFL scenarios, forming appropriate clusters can help build more personalized models for different groups. However, previous CFL approaches still face challenges from model heterogeneity. To further mitigate the heterogeneity problem, we propose a Mahalanobis distance based clustered federated learning (MD-CFL) method, which offers advantages in reducing model heterogeneity and improving clustering performance by correcting for feature skew in non-normalized data. Our experiments show that MD-CFL achieves accurate clustering performance, with a higher silhouette score compared to cosine-based FedAvg.