Dermatological diseases pose a major threat to the global health, affecting almost one-third of the world's population. Various studies have demonstrated that early diagnosis and intervention are often critical to prognosis and outcome. To this end, the past decade has witnessed the rapid evolvement of deep learning based smartphone apps, which allow users to conveniently and timely identify issues that have emerged around their skins. In order to collect sufficient data needed by deep learning and at the same time protect patient privacy, federated learning is often used, where individual clients aggregate a global model while keeping datasets local. However, existing federated learning frameworks are mostly designed to optimize the overall performance, while common dermatological datasets are heavily imbalanced. When applying federated learning to such datasets, significant disparities in diagnosis accuracy may occur. To address such a fairness issue, this paper proposes a fairness-aware federated learning framework for dermatological disease diagnosis. The framework is divided into two stages: In the first in-FL stage, clients with different skin types are trained in a federated learning process to construct a global model for all skin types. An automatic weight aggregator is used in this process to assign higher weights to the client with higher loss, and the intensity of the aggregator is determined by the level of difference between losses. In the latter post-FL stage, each client fine-tune its personalized model based on the global model in the in-FL stage. To achieve better fairness, models from different epochs are selected for each client to keep the accuracy difference of different skin types within 0.05. Experiments indicate that our proposed framework effectively improves both fairness and accuracy compared with the state-of-the-art.
翻译:皮肤病对全球健康构成重大威胁,影响到世界人口近三分之一的人口。各种研究表明,早期诊断和干预往往对预测和结果至关重要。为此,过去十年里,基于深层学习的智能手机应用程序迅速演变,使用户能够方便而及时地发现其皮肤周围出现的问题。为了收集深层学习所需的足够数据,同时保护患者隐私,常常使用联邦学习,个人客户在保持本地数据集的同时收集了一个全球模型。然而,现有联合学习框架的设计大多是为了优化总体准确性能,而普通的皮肤病数据集则严重失衡。在应用基于深层学习的智能手机应用程序时,诊断准确性可能出现巨大差异。为了解决这样一个公平性问题,本文件建议为皮肤病诊断建立一个公平觉悟化的快化学习框架。这个框架分为两个阶段:在FL阶段,不同皮肤型客户的组合性模型在保持一个更精确的学习过程,在每一个更精确的阶段里程中,用更高层次的精确性模型来构建一个更精确性模型,在每组内,用更高层次的内,用更高层次的内,用更高层次的指数来计算。