Help ?

IGMIN: あなたがここにいてくれて嬉しいです. お願いクリック '新しいクエリを作成してください' 当ウェブサイトへの初めてのご訪問で、さらに情報が必要な場合は.

すでに私たちのネットワークのメンバーで、すでに提出した質問に関する進展を追跡する必要がある場合は, クリック '私のクエリに連れて行ってください.'

Search

Organised by  IgMin Fevicon

Regional sites

Explore Section

Content for the explore section slider goes here.

Abstract

要約 at IgMin Research

We strive to bridge various fields of science and drive the rapid evolution of research and understanding.

Engineering Group Research Article 記事ID: igmin112

Federated Learning- Hope and Scope

Machine Learning Data EngineeringArtificial Intelligence DOI10.61927/igmin112 Affiliation

Affiliation

    Lhamu Sherpa, Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim, India, Email: lhamusherpa1206@gmail.com

5.8k
VIEWS
1.1k
DOWNLOADS
Connect with Us

要約

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.

数字

参考文献

    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.

類似の記事

The Educational Role of Cinema in Physical Sciences
Maria Sagri, Denis Vavougios and Filippos Sofos
DOI10.61927/igmin121
Diagnostic Challenges in Pancreatic Tumors
Ionuţ Simion Coman, Elena Violeta Coman, Costin George Florea, Teodora Elena Tudose, Cosmin Burleanu, Anwar Erchid and Valentin Titus Grigorean
DOI10.61927/igmin185

Why publish with us?

  • Global Visibility – Indexed in major databases

  • Fast Peer Review – Decision within 14–21 days

  • Open Access – Maximize readership and citation

  • Multidisciplinary Scope – Biology, Medicine and Engineering

  • Editorial Board Excellence – Global experts involved

  • University Library Indexing – Via OCLC

  • Permanent Archiving – CrossRef DOI

  • APC – Affordable APCs with discounts

  • Citation – High Citation Potential

Submit Your Article

Advertisement