Federated learning is a very convenient approach for scenarios where (i) the exchange of data implies privacy concerns and/or (ii) a quick reaction is needed. In smart healthcare systems, both aspects are usually required. In this paper, we work on the first scenario, where preserving privacy is key and, consequently, building a unique and massive medical image data set by fusing different data sets from different medical institutions or research centers (computation nodes) is not an option. We propose an ensemble federated learning (EFL) approach that is based on the following characteristics: First, each computation node works with a different data set (but of the same type). They work locally and apply an ensemble approach combining eight well-known CNN models (densenet169, mobilenetv2, xception, inceptionv3, vgg16, resnet50, densenet121, and resnet152v2) on Chest X-ray images. Second, the best two local models are used to create a local ensemble model that is shared with a central node. Third, the ensemble models are aggregated to obtain a global model, which is shared with the computation nodes to continue with a new iteration. This procedure continues until there are no changes in the best local models. We have performed different experiments to compare our approach with centralized ones (with or without an ensemble approach). The results conclude that our proposal outperforms these ones in Chest X-ray images (achieving an accuracy of 96.63%) and offers very competitive results compared to other proposals in the literature. A source code is provided at the Code Ocean repository: https://codeocean.com/capsule/0530602/treeXunta de Galicia | Ref. (Centro de investigación de Galicia accreditation 2019– 2022)Universidade de Vigo/CISUGMinisterio de Ciencia e Innovación | Ref. PID2020-113795RB-C3
