Improving personalized federated learning to optimize site-specific performance in computer-aided detection/diagnosis

Personalized federated learning (PFL) has been explored to address data heterogeneity while preserving privacy, and its application in computer-aided detection/diagnosis (CAD) software has been investigated. Ditto, a commonly studied PFL method, trains global and personalized models but is limited by instability in model updates and high hyperparameter tuning costs. We proposed Improved Ditto, a PFL method that dynamically adjusts the proportion of global model weights during personalized model updates to enhance stability and reduce hyperparameter tuning costs.

We introduced a personalized model update rule in Improved Ditto that dynamically determines the proportion of global model weights based on the L2-norm of the gradient-derived and global-model-derived terms. This method was evaluated using three types of CAD software: cerebral aneurysm detection in magnetic resonance (MR) angiography images (segmentation), brain metastasis detection in contrast-enhanced T1-weighted MR images (object detection), and liver lesion classification in gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced MR images (classification). The proposed method was compared with several conventional methods.

In two out of three CAD software, the performance of Improved Ditto was competitive with Ditto and other federated-learning-based methods. The proposed method achieved a narrower hyperparameter search space, which contributed to reducing the tuning costs. In addition, it improved the stability of personalized model updates, suggesting enhanced adaptability to diverse datasets and tasks.

We demonstrate that dynamically adjusting global model weights during personalized model updates can improve the stability and adaptability of PFL. The proposed method reduces the hyperparameter tuning costs and offers potential benefits for CAD software.