Privacy-Preserving Computing in the Healthcare Using Federated Learning
Abstract
In the context of healthcare, the Internet of Things (IoT) relates to a collection of physically linked objects that may share data. This network comprises a variety of medical equipment, wearables, and sensors. The Internet of Medical Things (IoMT) concentrates exclusively on IoT applications in the medical field, with an emphasis on gadgets connected to patient care and health monitoring. Large volumes of health-related data are generated by these networked devices, which introduces the concept of big data. Patient records, measurements from real-time monitoring, and other health-related data are all included in this data. Artificial intelligence's machine learning subfield enhances big data analytics by allowing computers to learn from the data they collect and gradually become more efficient. Machine learning algorithms may be used in healthcare for illness detection, therapy optimization, customized medication, and analytical forecasting. There are several possible advantages to integrating these technologies in healthcare. Proactive intervention, early anomaly identification, and continuous health tracking are made possible by real-time monitoring via IoT/IoMT devices. But there are additional issues with data security, privacy, interoperability, and the moral use of patient data that come with this intersection. To deal with such issues, federated learning (FL) is playing a very crucial role in privacy-preserving machine learning (PPML) in the smart healthcare…
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