论文标题
用于医疗IoT设备的联合学习框架
A Federated Learning Framework for Healthcare IoT devices
论文作者
论文摘要
物联网（IoT）革命显示出可能引起许多医疗应用，并获得了物联网设备收集的大量医疗保健数据。但是，对医疗数据隐私和安全性的需求不断增长，使每个物联网设备都是孤立的数据岛。此外，可穿戴医疗设备的有限计算和通信能力限制了纳利亚联合学习的应用。为此，我们提出了一个高级联合学习框架，以训练深层神经网络，在该网络中，网络被分配并分配给IoT设备和集中式服务器。然后，大多数培训计算由功能强大的服务器处理。激活和梯度的稀疏大大减少了开销的交流。实证研究表明，所提出的框架可以保证较低的精度损失，而在联合学习的香草中只需要0.2％的同步流量。
The Internet of Things (IoT) revolution has shown potential to give rise to many medical applications with access to large volumes of healthcare data collected by IoT devices. However, the increasing demand for healthcare data privacy and security makes each IoT device an isolated island of data. Further, the limited computation and communication capacity of wearable healthcare devices restrict the application of vanilla federated learning. To this end, we propose an advanced federated learning framework to train deep neural networks, where the network is partitioned and allocated to IoT devices and a centralized server. Then most of the training computation is handled by the powerful server. The sparsification of activations and gradients significantly reduces the communication overhead. Empirical study have suggested that the proposed framework guarantees a low accuracy loss, while only requiring 0.2% of the synchronization traffic in vanilla federated learning.
