| Title | Privacy-Preserving Federated Learning for Distributed Healthcare Analytics: A Secure Collaborative Framework |
| Research Area | Federated Learning |
| Abstract | The increasing adoption of digital healthcare systems has generated large quantities of patient-re-lated information across hospitals, diagnostic centers, research institutions, and specialized medical facilities. Machine learning can analyze such information to support disease prediction, clinical deci-sion-making, medical image interpretation, and patient risk assessment. However, conventional cen-tralized machine-learning approaches require healthcare institutions to transfer data to a common re-pository. Such data movement can create privacy, security, governance, and institutional trust chal-lenges. Federated learning provides an alternative approach by allowing participating organizations to train machine-learning models locally while sharing model updates rather than directly sharing their underlying datasets.
This paper proposes a privacy-preserving federated learning framework for distributed healthcare analytics. The proposed architecture combines local model training, secure aggregation, participant authentication, differential privacy, update validation, and adaptive communication strategies. In-stead of transferring raw patient records to a central server, each participating institution trains a lo-cal model using its own data. Model parameters or protected updates are then transmitted to an ag-gregation service, which combines the contributions to produce an improved global model. The re-sulting model can subsequently be distributed to participating institutions for another training round. The paper examines important challenges associated with healthcare federated learning, including non-independent data distributions, heterogeneous computing resources, communication overhead, malicious participants, model poisoning, inference attacks, and privacy leakage. A conceptual trust and risk model is introduced to evaluate participant updates before aggregation |
| Keywords | Federated Learning, Healthcare Analytics, Privacy Preservation, Machine Learning, Distributed Learning, Secure Aggregation, Differential Privacy, Healthcare Data, Model Poisoning, Medical Artificial Intelligence |
| Paper Status | Published |
| Volume | 2 |
| Issue | 3 |
| Published On | 20/06/2026 |
| Published File |
IJSRTD_4884.pdf
|