Federated Learning System for Privacy-Preserving Clinical AI: Architecture and Use Case Overview

This technical diagram illustrates a federated learning (FL) system designed for privacy-preserving AI applications in healthcare. The architecture allows hospitals and clinics to collaboratively train machine learning models without sharing raw patient data. Each institution trains the model locally on-site and sends encrypted updates to a central server for aggregation, supporting compliance with HIPAA, HITECH, and CCPA regulations. The system incorporates optional enhancements such as differential privacy and secure aggregation to ensure end-to-end data security.

This figure is part of a larger research and implementation initiative under MedSypher, a privacy-first healthcare AI platform founded by Shri Sai Nithin Chowdhary Dukkipati. Use cases supported by this system include early sepsis detection, hospital readmission risk prediction, chronic disease progression monitoring, and triage optimization.

This visual is published alongside a brief explanatory PDF to help clinicians, developers, and health IT leaders understand how federated AI systems can be deployed responsibly and effectively in real-world clinical environments.