  The mouth, often regarded as a window to the internal state of the body,
plays an important role in reflecting one's overall health. Poor oral hygiene
has far-reaching consequences, contributing to severe conditions like heart
disease, cancer, and diabetes, while inadequate care leads to discomfort, pain,
and costly treatments. Federated Learning (FL) for object detection can be
utilized for this use case due to the sensitivity of the oral image data of the
patients. FL ensures data privacy by storing the images used for object
detection on the local device and trains the model on the edge. The updated
weights are federated to a central server where all the collected weights are
updated via The Federated Averaging algorithm. Finally, we have developed a
mobile app named OralH which provides user-friendly solutions, allowing people
to conduct self-assessments through mouth scans and providing quick oral health
insights. Upon detection of the issues, the application alerts the user about
potential oral health concerns or diseases and provides details about dental
clinics in the user's locality. Designed as a Progressive Web Application
(PWA), the platform ensures ubiquitous access, catering to users across devices
for a seamless experience. The application aims to provide state-of-the-art
segmentation and detection techniques, leveraging the YOLOv8 object detection
model to identify oral hygiene issues and diseases. This study deals with the
benefits of leveraging FL in healthcare with promising real-world results.
