Integrating Federated Learning and IoT for Privacy-Preserving Smart Healthcare Systems

Significant privacy issues have been raised by the massive amounts of sensitive health data generated by the Internet of Things' (IoT) rapid proliferation of smart healthcare systems. Researchers are looking into integrating federated learning (FL) with IoT for smart healthcare applications in order to overcome these issues. Federated learning is a machine learning technique that enables dispersed organizations or devices to work together to build a common model without explicitly sharing local data. This preserves user privacy while facilitating the creation of robust models. FL makes it easier to train machine learning models directly on decentralized IoT devices in smart healthcare, protecting patient privacy by preventing the transmission of raw data. Additionally, FL and blockchain technology improve data security, privacy, and integrity by offering a decentralized model management mechanism. Establishing the groundwork for a revolutionary future in tailored medicine and early illness diagnosis, this FL and IoT integration holds significant promise for creating sophisticated, privacy-preserving smart healthcare systems that enable proactive, customized healthcare treatments.