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Federated Learning (FL) is a promising machine learning approach for development of data-driven global model using collaborative local models across multiple local institutions. However, the heterogeneity of medical imaging data is one of the challenges within FL. This heterogeneity is caused by the variation in imaging scanner protocols across institutions, which may result in weight shift among local models leading to deterioration in predictive accuracy of global model. The prevailing approaches involve applying different FL averaging techniques to enhance the performance of the global model, ignoring the distinct imaging features of the local domain. In this work, we address both the local and global model weight shift by introducing multiscale amplitude harmonization of the imaging in the local models utilizing Haar and harmonic wavelets. First, we tackle the local model weight shift by transforming the image feature space into multiscale frequency space using multiscale based harmonization. This aims to achieve harmonized image feature space across local models. Second, based on harmonized image feature space, a weighted regularization term is applied to local models, effectively mitigating weight shifts within these models. This weighted regularization assists in managing global model shifts by aggregating the optimized local models. We evaluate the proposed method using publicly available histopathological dataset MoNuSAC2018, TNBC for nuclei segmentation, and Camelyon17 dataset for tumor tissue classification. The average testing accuracies are 96.55%, and 92.47% for classification of tumor tissue while Dice co-efficients are 84.33%, and 84.46% for segmentation of nuclei with Haar and harmonic multiscale based harmonization, respectively. The comparison results for nuclei segmentation and tumor tissue classification using histopathological data show that our proposed methods perform better than the state-of-the-art FL methods.
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