TY - GEN
T1 - Federated Active Learning with Transfer Learning
T2 - International Wireless Communications and Mobile Computing
AU - Babar, Farah Farid
AU - Jamil, Faisal
AU - Alsboui, Tariq
AU - Babar, Faiza Fareed
AU - Ahmad, Shabir
AU - Alkanhel, Reem Ibrahim
N1 - Funding Information:
The authors would like to acknowledge the support of the University of Huddersfield for paying the Article Processing Charges (APC) of this publication. Special acknowledgment to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R323), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Publisher Copyright:
© 2024 IEEE.
PY - 2024/7/17
Y1 - 2024/7/17
N2 - Federated Learning has emerged as a promising paradigm for collaborative model training in healthcare. FL allows institutions to share knowledge without compromising patient privacy. However, data annotation remains a bottleneck, especially in medical image studies. This work proposes a Federated Active Learning with a Transfer Learning framework for efficient labeling in lung cancer diagnosis. Using ensemble entropy-based uncertainty assessment, FAL-TL streamlines sample annotation, optimizing training across distributed healthcare institutions while safeguarding patient privacy. Using the IQOTH/NCCD Lung Cancer and Chest CT-Scan images Dataset, our FAL-TL framework achieves an impressive 99.20% accuracy, surpassing traditional machine learning models. By integrating transfer learning, FAL-TL adapts pre-trained models to healthcare datasets, significantly enhancing diagnostic accuracy. This research contributes to advancing FL techniques in healthcare, offering a scalable and privacy-preserving solution with transformative implications for diagnostics and patient care.
AB - Federated Learning has emerged as a promising paradigm for collaborative model training in healthcare. FL allows institutions to share knowledge without compromising patient privacy. However, data annotation remains a bottleneck, especially in medical image studies. This work proposes a Federated Active Learning with a Transfer Learning framework for efficient labeling in lung cancer diagnosis. Using ensemble entropy-based uncertainty assessment, FAL-TL streamlines sample annotation, optimizing training across distributed healthcare institutions while safeguarding patient privacy. Using the IQOTH/NCCD Lung Cancer and Chest CT-Scan images Dataset, our FAL-TL framework achieves an impressive 99.20% accuracy, surpassing traditional machine learning models. By integrating transfer learning, FAL-TL adapts pre-trained models to healthcare datasets, significantly enhancing diagnostic accuracy. This research contributes to advancing FL techniques in healthcare, offering a scalable and privacy-preserving solution with transformative implications for diagnostics and patient care.
KW - Federated learning
KW - Edge Intelligence
KW - Lung Cancer Diagnosis
KW - Diagnostic Precision
KW - Active Learning
KW - Transfer Learning
KW - Federated Learning
UR - http://www.scopus.com/inward/record.url?scp=85200008338&partnerID=8YFLogxK
U2 - 10.1109/IWCMC61514.2024.10592390
DO - 10.1109/IWCMC61514.2024.10592390
M3 - Conference contribution
SN - 9798350361278
T3 - International Wireless Communications and Mobile Computing (IWCMC)
SP - 1333
EP - 1338
BT - 20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024
PB - IEEE
Y2 - 27 May 2024 through 31 May 2024
ER -
