Efficient federated learning for pediatric pneumonia on chest X-ray classification

Pappa, S. et al. Prevalence of depression, anxiety, and insomnia among healthcare workers during the covid-19 pandemic: A systematic review and meta-analysis. Brain Behav. Immun. 88, 901–907 (2020).
Google Scholar
Pham, H. T., Nguyen, P. T., Tran, S. T. & Phung, T. T. Clinical and pathogenic characteristics of lower respiratory tract infection treated at the Vietnam national children’s hospital. Can. J. Infect. Dis. Med. Microbiol. 2020, 1–6 (2020).
Google Scholar
Pernica, J. M. et al. Short-course antimicrobial therapy for pediatric community-acquired pneumonia: the safer randomized clinical trial. JAMA Pediatr. 175, 475–482 (2021).
Google Scholar
Ouyang, X. et al. Dual-sampling attention network for diagnosis of covid-19 from community acquired pneumonia. IEEE Trans. Med. Imaging 39, 2595–2605 (2020).
Google Scholar
Duron, L. et al. Assessment of an AI aid in detection of adult appendicular skeletal fractures by emergency physicians and radiologists: a multicenter cross-sectional diagnostic study. Radiology 300, 120–129 (2021).
Google Scholar
Jain, R., Gupta, M., Taneja, S. & Hemanth, D. J. Deep learning based detection and analysis of covid-19 on chest x-ray images. Appl. Intell. 51, 1690–1700 (2021).
Google Scholar
Liang, H. et al. Children’s pneumonia diagnosis system based on Mach–Zehnder optical fiber sensing technology. Int. J. Biomed. Eng. 207–212 (2022).
Masud, M. et al. A pneumonia diagnosis scheme based on hybrid features extracted from chest radiographs using an ensemble learning algorithm. J. Healthc. Eng. 2021 (2021).
Effah, C. Y. et al. Machine learning-assisted prediction of pneumonia based on non-invasive measures. Front. Public Health 10, 938801 (2022).
Google Scholar
Sarker, I. H. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2, 420 (2021).
Google Scholar
Wu, X. et al. A novel centralized federated deep fuzzy neural network with multi-objectives neural architecture search for epistatic detection. IEEE Trans. Fuzzy Syst. (2024).
Rajpurkar, P. et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017).
Kundu, R., Das, R., Geem, Z. W., Han, G.-T. & Sarkar, R. Pneumonia detection in chest x-ray images using an ensemble of deep learning models. PLoS One 16, e0256630 (2021).
Google Scholar
Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R. & Mittal, A. Pneumonia detection using CNN based feature extraction. In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) 1–7 (IEEE, 2019).
Nithya, T., Kanna, P. R., Vanithamani, S. & Santhi, P. An efficient pm-multisampling image filtering with enhanced CNN architecture for pneumonia classification. Biomed. Signal Process. Control 86, 105296 (2023).
Google Scholar
Piccialli, F., Di Somma, V., Giampaolo, F., Cuomo, S. & Fortino, G. A survey on deep learning in medicine: Why, how and when?. Inf. Fusion 66, 111–137 (2021).
Google Scholar
Hu, K. et al. Federated learning: a distributed shared machine learning method. Complexity 2021, 1–20 (2021).
Google Scholar
Wu, X., Wang, H., Shi, M., Wang, A. & Xia, K. DNA motif finding method without protection can leak user privacy. IEEE Access 7, 152076–152087 (2019).
Google Scholar
Zhao, Z., Yang, F. & Liang, G. Federated learning based on diffusion model to cope with non-iid data. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 220–231 (Springer, 2023).
Li, T. et al. Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429–450 (2020).
Karimireddy, S. P. et al. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning 5132–5143 (PMLR, 2020).
Lee, G., Jeong, M., Shin, Y., Bae, S. & Yun, S.-Y. Preservation of the global knowledge by not-true distillation in federated learning. Adv. Neural. Inf. Process. Syst. 35, 38461–38474 (2022).
Shoham, N. et al. Overcoming forgetting in federated learning on non-iid data. arXiv preprint arXiv:1910.07796 (2019).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012).
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
Konečnỳ, J., McMahan, H. B., Ramage, D. & Richtárik, P. Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527 (2016).
Liu, Q., Chen, C., Qin, J., Dou, Q. & Heng, P.-A. Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 1013–1023 (2021).
Kuo, K. M., Talley, P. C., Huang, C. H. & Cheng, L. C. Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach. BMC Med. Inform. Decis. Mak. 19, 1–8 (2019).
Google Scholar
Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840–6851 (2020).
Morafah, M., Reisser, M., Lin, B. & Louizos, C. Stable diffusion-based data augmentation for federated learning with non-iid data. arXiv preprint arXiv:2405.07925 (2024).
Singh, A., Shalini, S. & Garg, R. Classification of pediatric pneumonia prediction approaches. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) 709–712 (IEEE, 2021).
Lissaman, C. et al. Prospective observational study of point-of-care ultrasound for diagnosing pneumonia. Arch. Dis. Child. 104, 12–18 (2019).
Google Scholar
Chattopadhyay, S., Kundu, R., Singh, P. K., Mirjalili, S. & Sarkar, R. Pneumonia detection from lung x-ray images using local search aided sine cosine algorithm based deep feature selection method. Int. J. Intell. Syst. 37, 3777–3814 (2022).
Google Scholar
Kumar, G. S. et al. Differential privacy scheme using laplace mechanism and statistical method computation in deep neural network for privacy preservation. Eng. Appl. Artif. Intell. 128, 107399 (2024).
Google Scholar
Kumar, G. S., Premalatha, K., Maheshwari, G. U. & Kanna, P. R. No more privacy concern: A privacy-chain based homomorphic encryption scheme and statistical method for privacy preservation of user’s private and sensitive data. Expert Syst. Appl. 234, 121071 (2023).
Google Scholar
Zhu, L., Liu, Z. & Han, S. Deep leakage from gradients. Adv. Neural Inf. Process. Syst. 32 (2019).
Dwork, C. Differential privacy. In International Colloquium on Automata, Languages, and Programming 1–12 (Springer, 2006).
Ogburn, M., Turner, C. & Dahal, P. Homomorphic encryption. Proc. Comput. Sci. 20, 502–509 (2013).
Google Scholar
Goldreich, O. Secure multi-party computation. Manuscript. Preliminary version. 78, 1–108 (1998).
link