Machine Learning Based Security for Healthcare Data Transmission in IoT Wireless Networks

IoT coupled with wireless networks has transformed real-time patient monitoring and healthcare data transmission. Fast and safe data transmission is somewhat challenging to provide given issues with network protocols and the threat of cyberattacks. This work intends to guarantee healthcare data transmission in wireless IoT environments by presenting a method grounded on safe machine learning. The technology employs federated learning, models to find abnormalities, and innovative encryption methods to make data more safe. The suggested system may real-time evaluate healthcare data in order to identify and stop unauthorised access, data breaches, and hostile assaults. Using pertinent performance criteria, our analysis reveals that security and efficiency improve very significantly when compared to traditional methods. Reliability of healthcare IoT systems depends on strict privacy rules, hence scalable security solutions are made feasible as well.