FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification

TL;DR

FedSDA introduces a novel federated learning approach that aligns stain distributions across clients to address non-IID challenges in histopathological image classification, improving model performance while preserving privacy.

Contribution

The paper proposes FedSDA, a stain distribution alignment method using diffusion models and stain separation, specifically designed to mitigate non-IID data issues in federated histopathological image analysis.

Findings

FedSDA outperforms existing baselines in non-IID histopathological classification.

Aligning stain distributions improves federated model convergence and accuracy.

The method effectively reduces distribution shifts without compromising data privacy.

Abstract

Federated learning (FL) has shown success in collaboratively training a model among decentralized data resources without directly sharing privacy-sensitive training data. Despite recent advances, non-IID (non-independent and identically distributed) data poses an inevitable challenge that hinders the use of FL. In this work, we address the issue of non-IID histopathological images with feature distribution shifts from an intuitive perspective that has only received limited attention. Specifically, we address this issue from the perspective of data distribution by solely adjusting the data distributions of all clients. Building on the success of diffusion models in fitting data distributions and leveraging stain separation to extract the pivotal features that are closely related to the non-IID properties of histopathological images, we propose a Federated Stain Distribution Alignment (FedSDA) method. FedSDA aligns the stain distribution of each client with a target distribution in an FL framework to mitigate distribution shifts among clients. Furthermore, considering that training diffusion models on raw data in FL has been shown to be susceptible to privacy leakage risks, we circumvent this problem while still effectively achieving alignment. Extensive experimental results show that FedSDA is not only effective in improving baselines that focus on mitigating disparities across clients' model updates but also outperforms baselines that address the non-IID data issues from the perspective of data distribution. We show that FedSDA provides valuable and practical insights for the computational pathology community.