Abstract
During the previous several decades, data generation has an exponential growth due to increase in use of the Internet and smart devices. Also there is a huge increase in utilization of online media due to very cost-effective and high speed availability of the Internet which indirectly generates massive data. This increase in data leads to problems of data storage and processing of that data. For massive data storage, big data technologies and cloud storage resolved the problem to some extent. Nowadays, researchers started working on analyzing that data and producing useful results which will benefit the business, society, and to the world. Data processing for large-scale applications has been transformed by machine learning. Simultaneously, due to rising privacy concerns in various trending applications on online platforms, traditional data training techniques were redesigned. Classic machine learning, in particular, entails centralized model training, in which data is collected, and that central server is responsible for the whole training procedure. When information is sent to the main cloud server, it poses many privacy risks to participants. Here, federated learning can significantly overcome privacy issues over traditional centralized machine learning processes. The federated learning approach in machine learning enables users to train a model at local level without transmitting the user’s private data to the central data server. Here, we will implement an innovative federated learning model, also will evaluate output parameters of it with a traditional machine learning approach. For the implementation, we are using the publicly available healthcare dataset. Here we are safeguarding the privacy of participants. We believe that this study will demonstrate the advantages of federated learning and encourage its widespread adoption.
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References
Das S, Dey A, Pal A, Roy N (2015) Applications of artificial intelligence in machine learning: review and prospect. Int J Comput Appl 115(9)
Cockburn IM, Henderson R, Stern S (2018) The impact of artificial intelligence on innovation. National Bureau of Economic Research, Tech. Rep.
Kuang B, Fu A, Yu S, Yang G, Su M, Zhang Y (2019) Esdra: an efficient and secure distributed remote attestation scheme for IoT swarms. IEEE Internet Things J 6(5):8372–8383
Yu S, Wang G, Liu X, Niu J (2018) Security and privacy in the age of the smart internet of things: an overview from a networking perspective. IEEE Commun Mag 56(9):14–18
Yu R, Xue G, Zhang X (2018) Application provisioning in FOG computing-enabled internet-of-things: a network perspective. In: Proceedings of international conference on computer communications (INFOCOM), pp 783–791
Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 10(2):12
McMahan B, Moore E, Ramage D, Hampson S, Arcas BAY (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics. PMLR, 2017, pp 1273–1282
Samarakoon S, Bennis M, Saad W, Debbah M (2019) Distributed federated learning for ultra reliable low-latency vehicular communications. IEEE Trans Commun 68(2):1146–1159
Lyu L, Yu H, Yang Q (2020) Threats to federated learning: a survey. preprint. arXiv:2003.02133
Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1–19
Al-Rubaie M, Chang JM (2019) Privacy-preserving machine learning: threats and solutions. IEEE Security Privacy 17(2):49–58
Papernot N, McDaniel P, Sinha A, Wellman M (2016) Towards the science of security and privacy in machine learning. arXiv preprint arXiv:1611.03814
Kamarinou D, Millard C, Singh J (2016) Machine learning with personal data. Queen Mary School of Law Legal Studies Research Paper, no. 247
Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Konecny J, Azzocchi S, McMahan HB et al. (2019) Towards federated learning at scale: system design. arXiv preprint arXiv:1902.01046
Smith V, Chiang C-K, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4424–4434
Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K (2017) Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pp 1175–1191
Bourke C, Douglas K, Porter R (2010) Processing of facial emotion expression in major depression: a review. Aust N Z J Psychiatry 44:681–696
Ozkanca Y, Ozt ¨urk MG, Nur Ekmekci M, ¨Atkins DC, Demiroglu C, Hosseini Ghomi R (2019) Depression screening from voice samples of patients affected by Parkinson’s disease. Digital Biomarkers 3:72–82
Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Br J Psychiatry 65(6):386–408
Cybenko G (1989) Approximations by superpositions of sigmoidal functions. Mathem Controls Signals Syst 2(4):303–314
Efron B, Hastie T (2016) Computer age statistical inference: algorithms, evidence, and data science. Cambridge University Press
Pham TD (2020) A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks. Sci Reports 10:16942
Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Reports 10:19549
Mukherjee H, Ghosh S, Dhar A, Obaidullah SM, Santosh KC, Roy K (2020) Deep neural network to detect COVID-19: one architecture for both CT scans and chest X-rays. Appl Intell
Rauf HT, Lali MIU, Khan MA, Kadry S, Alolaiyan H, Razaq A, Irfan R (2021) Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks. Personal and Ubiquitous Comput
Hassan A, Shahin I, Alsabek MB (2020) COVID-19 detection system using recurrent neural networks. In 2020 International conference on communications, computing, cybersecurity, and informatics (CCCI), pp 1–5
Gers FA, Schraudolph NN, Schmidhuber J (2002) Learning precise timing with LSTM recurrent networks. J Machine Learn Res 3:115–143
Bayer J, Wierstra D, Togelius J, Schmidhuber J (2009) Evolving memory cell structures for sequence learning. In: International conference on artificial neural networks. Springer, pp 755–764
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. The MIT Press
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks IV, pp 1942–1948
Forman G, Suermondt HJ (2010) Retraining a machine-learning classifier using re-labeled training samples/ US Patent 7,792,353, 7 Sep 2010
Duan M, Liu D, Chen X, Tan Y, Ren J, Qiao L, Liang L (2019) Astraea: self- balancing federated learning for improving classification accuracy of mobile deep learning applications. In: 2019 IEEE 37th international conference on computer design (ICCD). IEEE, pp 246–254
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Govindwar, G.D., Dhande, S.S. (2023). An Approach of Federated Learning in Artificial Intelligence for Healthcare Analysis. In: Kumar, S., Hiranwal, S., Purohit, S., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies. ICCCT 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-3485-0_8
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