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)\color{black}. 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.

通过集成联邦学习方法，在医疗图像数据集隐私保护的前提下，在胸部 X 线图像中取得了 96.63％的准确度，并在文献中与其他方法取得了相当竞争性的结果。

集成联邦学习：一种协作性肺炎诊断方法

Ensemble Federated Learning: an approach for collaborative pneumonia  diagnosis

Federated learning enables clients to collaboratively learn a shared global
model without sharing their local training data with a cloud server. However,
malicious clients can corrupt the global model to predict incorrect labels for
testing examples. Existing defenses against malicious clients leverage
Byzantine-robust federated learning methods. However, these methods cannot
provably guarantee that the predicted label for a testing example is not
affected by malicious clients. We bridge this gap via ensemble federated
learning. In particular, given any base federated learning algorithm, we use
the algorithm to learn multiple global models, each of which is learnt using a
randomly selected subset of clients. When predicting the label of a testing
example, we take majority vote among the global models. We show that our
ensemble federated learning with any base federated learning algorithm is
provably secure against malicious clients. Specifically, the label predicted by
our ensemble global model for a testing example is provably not affected by a
bounded number of malicious clients. Moreover, we show that our derived bound
is tight. We evaluate our method on MNIST and Human Activity Recognition
datasets. For instance, our method can achieve a certified accuracy of 88% on
MNIST when 20 out of 1,000 clients are malicious.

本文提出了一种使用集成学习做到在联邦学习中抵抗恶意攻击的方法，它使用多个模型进行多数表决确定预测结果，测试表明该方法可以在受到最多 20 个恶意客户端攻击时在 MNIST 数据集上获得 88％的可靠准确度。