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The Internet of Things (IoT) revolution has shown potential to give rise tomany medical applications with access to large volumes of healthcare datacollected by IoT devices. However, the increasing demand for healthcare dataprivacy and security makes each IoT device an isolated island of data. Further,the limited computation and communication capacity of wearable healthcaredevices restrict the application of vanilla federated learning. To this end, wepropose an advanced federated learning framework to train deep neural networks,where the network is partitioned and allocated to IoT devices and a centralizedserver. Then most of the training computation is handled by the powerfulserver. The sparsification of activations and gradients significantly reducesthe communication overhead. Empirical study have suggested that the proposedframework guarantees a low accuracy loss, while only requiring 0.2% of thesynchronization traffic in vanilla federated learning.

A Federated Learning Framework for Healthcare IoT devices