Federated learning (FL) aided health diagnostic models can incorporate data from a large number of personal edge devices (e.g., mobile phones) while keeping the data local to the originating devices, largely ensuring privacy. However, such a cross-device FL approach for health diagnostics still imposes many challenges due to both local data imbalance (as extreme as local data consists of a single disease class) and global data imbalance (the disease prevalence is generally low in a population). Since the federated server has no access to data distribution information, it is not trivial to solve the imbalance issue towards an unbiased model. In this paper, we propose FedLoss, a novel cross-device FL framework for health diagnostics. Here the federated server averages the models trained on edge devices according to the predictive loss on the local data, rather than using only the number of samples as weights. As the predictive loss better quantifies the data distribution at a device, FedLoss alleviates the impact of data imbalance. Through a real-world dataset on respiratory sound and symptom-based COVID-$19$ detection task, we validate the superiority of FedLoss. It achieves competitive COVID-$19$ detection performance compared to a centralised model with an AUC-ROC of $79\%$. It also outperforms the state-of-the-art FL baselines in sensitivity and convergence speed. Our work not only demonstrates the promise of federated COVID-$19$ detection but also paves the way to a plethora of mobile health model development in a privacy-preserving fashion.
翻译:联邦学习(FL) 辅助健康诊断模型可以将大量个人边缘设备(如移动电话)的数据纳入数据,同时将数据保存在本地,主要确保隐私;然而,这种对健康诊断的跨设备FL方法仍然带来许多挑战,因为当地数据不平衡(因为当地数据包含单一疾病类别,因此极端)和全球数据不平衡(在人口人口中疾病流行率一般较低)。由于联邦服务器无法获取数据分发信息,解决不平衡问题成为公正的模式并非微不足道。在本文件中,我们提议FedLos,这是一个全新的交叉检测功能,用于健康诊断的FLFL框架。在这里,Federation服务器根据当地数据的预测损失平均在边缘设备上培训的模型,而不是仅仅使用样本数量作为重量。由于预测性损失更好地量化设备的数据分布,FedLosts减轻了数据不平衡的影响。通过真实世界数据集,以COVID-19美元为基准的COVI-19美元为基准,我们还在Fed-C-C-19美元测试中以C-美元为基准的Slodeal-ral-exxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx的快速测试的快速的快速测试的快速测试的快速测试的快速测试测试性运行的测试性性测试性测试性测试性性运行的测试性能性能性能性能测试性能测试性能测试。</s>