An improved federated learning approach enhanced internet of health things framework for private decentralized distributed data

With the privacy protection increasingly being concerned, Data centralization often heavily causes a big risk of privacy protection, gradually, there is a prevailing trend to enhance the security performance by means of data decentralization, above all, for health care internet of things (IoT) data. Meanwhile, Federated learning has obvious privacy advantages compared to data center training on protecting privacy data. For this reason, a novel framework based on federated learning is presented in this paper, which is suitable for private and decentralized data sets, such as big data in healthy Internet of Things. Specifically, the main work of the puts forward framework includes: (1) Multi-center data collection of healthy Internet of Things. (2) healthy data analysis of Internet of Things. (3) privacy protection method for data of healthy Internet of Things. Finally, related experiments show that the proposed method is feasible, and compared with the traditional methods, it has significantly improved the performance in Quality of Service (QoS) and IoUs indicator.