LUNG CANCER PREDICTION USING FEDERATED LEARNING

SUSHMIKA DAGGULA , SUCHETHANA KONDI , ADVAITH SRIDHAR

Keywords— Federated Learning, Healthcare Data, Data Privacy, Machine Learning, Deep Learning.

Abstract— Lung cancer remains a formidable health challenge worldwide, necessitating innovative approaches for early detection and personalized treatment. In this study, we propose a study federated learning system tailored for lung cancer prediction, integrating the collective intelligence of distributed healthcare institutions while preserving data privacy.Our approach emphasizes the strategic selection of features, encompassing clinical, imaging, and genetic data, to optimize model performance. Through iterative collaboration and model refinement, our federated system harnesses the strengths of each participating institution, resulting in a robust predictive model capable of accurately stratifying lung cancer risk. Our approach features secure data aggregation, integration of differential privacy techniques, and real-time threat monitoring, all designed to maintain the integrity and confidentiality of patient information throughout the learning processBy utilizing federated learning, our system overcomes issues related to data silos and regulatory restrictions, providing a scalable and privacy-conscious approach for predicting lung cancer. This research advances personalized medicine, with potential benefits for early intervention and enhanced patient outcomes in lung cancer treatment. Keywords— Federated Learning, Healthcare Data, Data Privacy, Machine Learning, Deep Learning.

Published Paper   E-Certificate

"LUNG CANCER PREDICTION USING FEDERATED LEARNING", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 4, page no.c754-c762, April-2025, Available :https://ijsdr.org/papers/IJSDR2504292.pdf

Volume 10 Issue 4, April-2025

Pages : c754-c762

Paper Reg. ID: IJSDR_302072

Published Paper Id: IJSDR2504292

Downloads: 000382

Research Area: Science and Technology

Country: Hyderabad, TELANGANA, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2504292

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2504292

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition