Federated Learning (FL) is a suitable solution for making use of sensitive data belonging to patients, people, companies, or industries that are obligatory to work under rigid privacy constraints. FL mainly or partially supports data privacy and security issues and provides an alternative to model problems facilitating multiple edge devices or organizations to contribute a training of a global model using a number of local data without having them. Non-IID data of FL caused from its distributed nature presents a significant performance degradation and stabilization skews. This paper introduces a novel method dynamically balancing the data distributions of clients by augmenting images to address the non-IID data problem of FL. The introduced method remarkably stabilizes the model training and improves the model's test accuracy from 83.22% to 89.43% for multi-chest diseases detection of chest X-ray images in highly non-IID FL setting. The results of IID, non-IID and non-IID with proposed method federated trainings demonstrated that the proposed method might help to encourage organizations or researchers in developing better systems to get values from data with respect to data privacy not only for healthcare but also other fields.

翻译：联邦学习联合会(FL)是使用病人、人、公司或行业在严格的隐私限制下必须工作的敏感数据的合适解决办法,FL主要或部分支持数据隐私和安全问题,并提供了一种替代模式,以便利多边缘装置或组织使用一些当地数据,协助培训全球模型,使用一些当地数据。FL的非IID数据因其分布性质而呈现显著的性能退化和稳定倾向。本文采用一种新颖的方法,通过增加图像以解决FL的非IID数据问题,积极平衡客户的数据分配。采用的方法明显稳定了模型培训,提高了模型测试的准确性,从83.22%到89.43%,用于在高度非IIDFL环境中检测胸部X光图像的多切疾病。IID、非IID和非IID的结果以及拟议的方法反馈培训表明,拟议的方法可能有助于鼓励组织或研究人员开发更好的系统,从数据隐私数据中获得价值,不仅用于医疗保健,而且用于其他领域。