International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Diabetes Prediction using Federated Learning |
|---|---|
| ABSTRACT | The rising prevalence of diabetes necessitates innovative approaches for accurate and privacy-preserving predictive systems. This paper explores the application of federated learning (FL) to develop a distributed, privacy-centric model for diabetes prediction. Unlike traditional centralized data collection methods, federated learning enables collaborative model training across multiple institutions without transferring sensitive patient data. The study highlights the integration of secure aggregation protocols and encryption techniques to ensure data confidentiality. Experimental results demonstrate the model’s capability to achieve high prediction accuracy while adhering to stringent privacy standards, thereby presenting a scalable solution for modern healthcare systems. |
| AUTHOR | YASHASWI R, KARTHIK B M, MANOJ GOWDA S, VENU K V |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311036 |
| pdf/36_Diabetes Prediction using Federated Learning.pdf | |
| KEYWORDS |
