REFERENCES
Ain, Q. U., Khan, M. A., Yaqoob, M. M., Khattak, U. F.,
Sajid, Z., Khan, M. I., & Al-Rasheed, A. 2023. Privacy-
Aware Collaborative Learning for Skin Cancer
Prediction. Diagnostics, 13(13), 2264.
Bdair, T., Navab, N., & Albarqouni, S. 2021. FedPerl: semi-
supervised peer learning for skin lesion classification.
In International Conference on Medical Image
Computing and Computer-Assisted Intervention (pp.
336-346). Cham: Springer International Publishing.
Chowdhury, A., Kassem, H., Padoy, N., Umeton, R., &
Karargyris, A. 2021. A review of medical federated
learning: Applications in oncology and cancer research.
In International MICCAI Brainlesion Workshop (pp. 3-
24). Cham: Springer International Publishing.
Deng, X., Oda, S., Kawano, Y. 2023. Graphene-based
midinfrared photodetector with bull’s eye plasmonic
antenna. Optical Engineering, 62(9), 097102-097102.
Duarte, A. F., Sousa-Pinto, B., Azevedo, L. F., Barros, A.
M., Puig, S., Malvehy, J., ... & Correia, O. 2021.
Clinical ABCDE rule for early melanoma detection.
European Journal of Dermatology, 31(6), 771-778.
Hameed, S. S., Hassan, W. H., Latiff, L. A., & Ghabban, F.
2021. A systematic review of security and privacy
issues in the internet of medical things; the role of
machine learning approaches. PeerJ Computer Science,
7, e414.
Hashmani, Manzoor Ahmed, et al. 2021. An adaptive
federated machine learning-based intelligent system for
skin disease detection: A step toward an intelligent
dermoscopy device. Applied Sciences, 11(5), 2145.
Hekler, A., Maron, R. C., Haggenmüller, S., Schmitt, M.,
Wies, C., Utikal, J. S., ... & Brinker, T. J. 2024. Using
multiple real-world dermoscopic photographs of one
lesion improves melanoma classification via deep
learning. Journal of the American Academy of
Dermatology.
Hosseini, S. M., Sikaroudi, M., Babaie, M., & Tizhoosh, H.
R. 2023. Proportionally fair hospital collaborations in
federated learning of histopathology images. IEEE
transactions on medical imaging.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., &
Bochtis, D. 2018. Machine learning in agriculture: A
review. Sensors, 18(8), 2674.
Liu, Y., Liu, L., Yang, L., Hao, L. and Bao, Y. 2021.
Measuring distance using ultra-wideband radio
technology enhanced by extreme gradient boosting
decision tree (XGBoost). Automation in Construction,
126, 103678.
Liu, Y. and Bao, Y. 2023. Intelligent monitoring of
spatially-distributed cracks using distributed fiber optic
sensors assisted by deep learning. Measurement, 220,
113418.
Qiu, Y., Wang, J., Jin, Z., Chen, H., Zhang, M., & Guo, L.
2022. Pose-guided matching based on deep learning for
assessing quality of action on rehabilitation training.
Biomedical Signal Processing and Control, 72, 103323.
Qiu, Y., Yang, Y., Lin, Z., Chen, P., Luo, Y., & Huang, W.
2020. Improved denoising autoencoder for maritime
image denoising and semantic segmentation of USV.
China Communications, 17(3), 46-57.
Riaz, S., Naeem, A., Malik, H., Naqvi, R. A., & Loh, W. K.
2023. Federated and Transfer Learning Methods for the
Classification of Melanoma and Nonmelanoma Skin
Cancers: A Prospective Study. Sensors, 23(20), 8457.
Roschewitz, David, et al. 2021. Ifedavg: Interpretable data-
interoperability for federated learning. arXiv preprint
arXiv:2107.06580.
Shi, Yujun, et al. 2023. Understanding and mitigating
dimensional collapse in federated learning. IEEE
Transactions on Pattern Analysis and Machine
Intelligence.
Sugaya, T., Deng, X. 2019. Resonant frequency tuning of
terahertz plasmonic structures based on solid
immersion method. 2019 44th International Conference
on Infrared, Millimeter, and Terahertz Waves, 1-2.
Tammina, S. 2019. Transfer learning using vgg-16 with
deep convolutional neural network for classifying
images. International Journal of Scientific and Research
Publications (IJSRP), 9(10), 143-150.
Wicaksana, J., Yan, Z., Yang, X., Liu, Y., Fan, L., & Cheng,
K. T. 2022. Customized federated learning for multi-
source decentralized medical image classification.
IEEE Journal of Biomedical and Health Informatics,
26(11), 5596-5607.
Xu, G., Wu, Y., Hu, J., & Shi, Y. 2022. Achieving fairness
in dermatological disease diagnosis through automatic
weight adjusting federated learning and personalization.
arXiv preprint arXiv:2208.11187.
Yaqoob, M. M., Alsulami, M., Khan, M. A., Alsadie, D.,
Saudagar, A. K. J., AlKhathami, M., & Khattak, U. F.
2023. Symmetry in privacy-based healthcare: a review
of skin cancer detection and classification using
federated learning. Symmetry, 15(7), 1369.
Yu, Z., Dong, Y., Cheng, J., Sun, M., & Su, F. 2022.
Research on face recognition classification based on
improved GoogleNet. Security and Communication
Networks, 2022, 1-6.
Yuan, Z. W., & Zhang, J. 2016. Feature extraction and
image retrieval based on AlexNet. In Eighth
International Conference on Digital Image Processing
(ICDIP 2016) (Vol. 10033, pp. 65-69). SPIE.
Zhao, F., Yu, F., Trull, T., & Shang, Y. 2023. A new
method using LLMs for keypoints generation in
qualitative data analysis. In 2023 IEEE Conference on
Artificial Intelligence (CAI) (pp. 333-334). IEEE.