References
- 1Alatawi, MN, et al. 2023. Cyber security against intrusion detection using ensemble-based approaches. Security and Communication Networks, 2023(SI): 1–7. DOI: 10.1155/2023/8048311
- 2Benhar, H, Idri, A and Fernández-Alemán, JL. 2020. Data preprocessing for heart diseaseclassification: A systematic literature review. Computer Methods and Programs in Biomedicine, 195: 105635. DOI: 10.1016/j.cmpb.2020.105635
- 3Bos, JW, Lauter, K and Naehrig, M. 2014. Private predictive analysis on encrypted medical data. Journal of Biomedical Informatics, 50: 234–243. DOI: 10.1016/j.jbi.2014.04.003
- 4Chollet, F. 2017. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258. DOI: 10.1109/CVPR.2017.195
- 5Choudhury, O, et al. 2020. Anonymizing data for privacy-preserving federated learning [online]. [Preprint]. [Viewed 21 February]. Available from: arXiv:2002.09096
- 6Domadiya, N and Udai, PR. 2021. Improving healthcare services using source anonymous scheme with privacy preserving distributed healthcare data collectionmining. Computing, 103(1): 155–177. DOI: 10.1007/s00607-020-00847-0
- 7Dwork, C. 2011. Differential privacy encyclopedia of cryptography and security. Boston: Springer. pp. 338–340. DOI: 10.1007/978-1-4419-5906-5_752
- 8Géron, A. 2019. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. Sebastopol, CA: O’Reilly Media.
- 9Google Colab. Available at
https://colab.research.google.com/ [Last accessed 02 May 2023]. - 10Hamza, R, et al. 2020. A privacy-preserving cryptosystem for IoT E-healthcare. Information Sciences, 527: 493–510. DOI: 10.1016/j.ins.2019.01.070
- 11He, K, et al. 2016b. Identity mappings in deep residual networks. In: European Conference on Computer Vision, Amsterdam, The Netherlands,
October 11–14, 2016 , pp. 630–645. DOI: 10.1007/978-3-319-46493-0_38 - 12He, K, Zhang, X, Ren, S and Sun, J. 2016a. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. DOI: 10.1109/CVPR.2016.90
- 13Howard, AG, et al. 2017. Mobilenets: E’cient convolutional neural networks for mobile vision applications [online]. [Preprint]. Available from arXiv:1704.04861. DOI: 10.1109/CVPR.2017.243
- 14Huang, G, et al. 2017. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA,
21–26 July 2017 , pp. 4700–4708. DOI: 10.1109/CVPR.2017.243 - 15Jamil, F and Kim, D. 2021. An Ensemble of a prediction and learning mechanism for improving accuracy of anomaly detection in network intrusion environments. Sustain, 13(18): 10057. DOI: 10.3390/su131810057
- 16Jena, M, Debaprada, et al. 2021. Intelligent Data Engineering and Analytics. Singapore: Springer. pp. 507–514. DOI: 10.1007/978-981-15-5679-1_49
- 17Krizhevsky, A, Sutskever, I and Hinton, GE. 2012. Imagenet classi¦cation with deep convolutional neural networks. Advances in neural information processing systems, 25.
- 18Le, QV, et al. 2011. On optimization methods for deep learning. In: International Conference on Machine Learning, Bellevue, Washington, USA,
June 28–July 2, 2011 , pp. 265–272. - 19Li, C, Li, G and Varshney, PK. 2022. Decentralized federated learning via mutual knowledge transfer. IEEE Internet of Things Journal, 9: 1136–1147. DOI: 10.1109/JIOT.2021.3078543
- 20Li, J, et al. 2021. A federated learning based privacy-preserving smart healthcare system. IEEE Transactions on Industrial Informatics, 18(3): 2021–2031. DOI: 10.1109/TII.2021.3098010
- 21Li, Z, et al. 2020. CrowdSFL: A secure crowd computing framework based on blockchain and federated learning. Electronics, 9(5): 773. DOI: 10.3390/electronics9050773
- 22McMahan, B and Ramage, D. 2017. Federated learning: Collaborative machine learning without centralized training data. Google Research Blog, [Blog].
- 23Paszke, A, et al. 2019. PyTorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32: 8024–8035.
- 24Pradeep, K, Kumar, M, et al. 2022. Privacy preserving blockchain with optimal deep learning model for smart cities. CMC, 73(3). 2 DOI: 10.32604/cmc.2022.030825
- 25Qiang, W, Liu R and Jin, H. 2021. Defending CNN against privacy leakage in edge computing via binary neural networks. Future Generation Computer Systems, 125: 460–470. DOI: 10.1016/j.future.2021.06.037
- 26Rahman, SA, et al. 2020. A Survey on federated learning: The journey from centralized to distributed on-site learning and beyond. IEEE Internet of Things Journal, early access.
- 27Rui, Hu, et al. 2020. Personalized federated learning with differential privacy. IEEE Internet of Things Journal, 7(10): 9530–9539. DOI: 10.1109/JIOT.2020.2991416
- 28Ryffel, T, et al. 2018. A generic framework for privacy preserving deep learning [online]. [Preprint] Available from: arXiv:1811.04017.
- 29Saba, T, et al. 2022. Securing the IoT system of smart city against cyber threats using deep learning. Discrete Dynamics in Nature and Society, 2022: 1–9. DOI: 10.1155/2022/1241122
- 30Shaham, S, et al. 2021. Privacy preserving location data publishing: A machine learning approach. IEEE Transactions on Knowledge and Data Engineering, 33(9): 3270–3283. DOI: 10.1109/TKDE.2020.2964658
- 31Simonyan, K and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition [online]. [Preprint]. Available from: arXiv:1409.1556.
- 32Singh, S, et al. 2022. A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology. Future Generation Computer Systems, 129: 380–388. DOI: 10.1016/j.future.2021.11.028
- 33Srivastava, GP, et al. 2021. PPSF: A privacy-preserving and secure framework using blockchain-based machinelearning for IoT-driven smart cities. IEEE Transactions on Network Science and Engineering, 8(3): 2326–2341. DOI: 10.1109/TNSE.2021.3089435
- 34Sun, Z, et al. 2019. Differential privacy for data and model publishing of medical ata. IEEE Access, 7: 152103–152114. DOI: 10.1109/ACCESS.2019.2947295
- 35Szegedy, C, et al. 2016. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826. DOI: 10.1109/CVPR.2016.308
- 36Tanwar, S, et al. 2020. Machine learning adoption in blockchain-based smart applications: The challenges and a way forward. IEEE Access, 8: 474–488. DOI: 10.1109/ACCESS.2019.2961372
- 37Toyoda, K, et al. 2020. Blockchain-enabled federated learning with mechanism design. IEEE Access, 8: 219744–219756. DOI: 10.1109/ACCESS.2020.3043037
- 38Wang, S, et al. 2019. Adaptive federated learning in resource constrained edge computing systems, IEEE Journal on Selected Areas in Communications, 37(6): 1205–1221. DOI: 10.1109/JSAC.2019.2904348
- 39Weng, J, et al. 2019. DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing, 18(5): 2438–2455. DOI: 10.1109/TDSC.2019.2952332
- 40Wu, X, et al. 2022. An adaptive federated learning scheme with differential privacy preserving. Future Generation Computer Systems, 127: 362–372. DOI: 10.1016/j.future.2021.09.015
- 41Yar, H, et al. 2021. Lung nodule detection and classification using 2D and 3D convolution neural networks (CNNs). Artificial Intelligence and Internet of Things, 2021: 365–386. DOI: 10.1201/9781003097204-17
- 42Yin, L, et al. 2021. A privacy-preserving federated learning for multiparty data sharing in social IoTs. IEEE Transactions on Network Science and Engineering, 8(3): 2706–2718. DOI: 10.1109/TNSE.2021.3074185
- 43Zhao, Y, et al. 2019. Machine learning based privacy-preserving fair data trading in big data market. Informing Science, 478: 449–460. DOI: 10.1109/ACCESS.2019.2909559
- 44Zhao, J, Chen, Y and Zhang, W. 2019. Differential privacy preservation in deep learning: Challenges, opportunities and solutions. IEEE Access, 7: 48901–48911.