IoT-Enabled Predictive Health Monitoring Using Federated Learning for Rural and Low-Resource Communities

Abstract

Rural areas have a hard time getting good healthcare because they don't have enough specialists, their diagnostic tools aren't reliable, and their health data systems are all over the place. Even though the Internet of Things can collect important health information through wearable devices, traditional machine learning models need to collect sensitive patient data in one place, which raises big concerns about privacy, legality, and trust. This study suggests a new way of doing things that combines health monitoring using the Internet of Things with something called federated learning. This approach allows for predictive analytics that protect patients' privacy, which is especially important in rural areas. What's different about our approach is that it keeps all the raw health data inside local clinics, so it's safe. Only encrypted updates to the model are shared, which helps build a global model that can predict diseases. We tackled some big challenges, like the fact that health data can look really different from one village to another, internet connections can be spotty, and local computers might not be powerful enough. Our framework can detect health problems like high blood pressure, diabetes, and other chronic conditions early on, all without compromising the privacy of patients' data. When we tested our approach, we found that it worked better than other methods, especially when the data was really different from one site to another. We also talked about what this means for making sure AI is used fairly in places with limited resources. Our goal is to make sure everyone has access to good healthcare, no matter where they live. We think this is a big step forward because it shows that we can use technology to improve healthcare in rural areas without putting patients' privacy at risk. By keeping data local and using federated learning, we can build models that are both accurate and trustworthy. This is especially important in rural areas, where people often have to travel far to get medical care. Our study has important implications for policymakers who want to make sure AI is used in a way that's fair and benefits everyone. We need to make sure that AI systems are designed with privacy and security in mind, especially when it comes to sensitive health data. By working together, we can create a healthcare system that's both high-tech and patient-centered.