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Abstract

Abstract at IgMin Research

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Federated Learning- Hope and Scope

Affiliation

Affiliation

    Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim, India

Abstract

People are suffering from” data obesity” as a result of the expansion and quick development of various Artificial Intelligence (AI) technologies and machine learning fields. The management of the current techniques is becoming more challenging due to the data created in the Smart-Health and Fintech service sectors. To provide stable and reliable methods for processing the data, several Machine Learning (ML) techniques were applied. Due to privacy-related issues with the aforementioned two providers, ML cannot fully use the data, which becomes difficult since it might not give the results that were expected. When the misuse and exploitation of personal data were gaining attention on a global scale and traditional machine learning (CML) was facing difficulties, Google introduced the concept of Federated Learning (FL). In order to enable the cooperative training of machine learning models among several organizations under privacy requirements, federated learning has been a popular research area. The expectation and potential of federated learning in terms of smart-health and fintech services are the main topics of this research.

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References

    1. Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: Concept and applications. 2019.
    2. Yang Q, Liu Y, Cheng Y, Kang Y, Chen T, Yu H. Federated Learning, ser. Synthesis Lectures on Artificial Intelligence and Machine Morgan & Claypool Publishers, 2019. https://books.google.co.in/books?id=JdPGDwAAQBAJ
    3. Long G, Tan Y, Jiang J, Zhang C. Federated learning for openbanking. 2021.
    4. Hussain GKJ, Manoj G. Federated learning: A survey of a new approach to machine learning. In 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). 2022; 1-8.
    5. Stanˇo M, Hluchy L, Boba´k M, Krammer P, Tran V. Federated learning methods for analytics of big and sensitive distributed data and survey. In 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI). 2023; 000 705–000
    6. Dasaradharami Reddy K, Gadekallu TR. A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics. Comput Intell Neurosci. 2023 Mar 1;2023:8393990. doi: 10.1155/2023/8393990. PMID: 36909974; PMCID: PMC9995203.

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