| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-06-11 |
| タイトル |
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タイトル |
FedFusion: Adaptive Model Fusion for Addressing Feature Discrepancies in Federated Credit Card Fraud Detection |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Credit card fraud |
| キーワード |
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主題Scheme |
Other |
|
主題 |
fraud detection system |
| キーワード |
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主題Scheme |
Other |
|
主題 |
federated learning |
| キーワード |
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主題Scheme |
Other |
|
主題 |
FedFusion |
| キーワード |
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主題Scheme |
Other |
|
主題 |
CNN |
| キーワード |
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主題Scheme |
Other |
|
主題 |
MLP |
| キーワード |
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主題Scheme |
Other |
|
主題 |
LSTM |
| キーワード |
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主題Scheme |
Other |
|
主題 |
data heterogeneity |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Ferdous Aurna, Nahid
Delwar Hossain, Md
Khan, Latifur
妙中, 雄三
門林, 雄基
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
The digitization of financial transactions has led to a rise in credit card fraud, necessitating robust measures to secure digital financial systems from fraudsters. Nevertheless, traditional centralized approaches for detecting such frauds, despite their effectiveness, often do not maintain the confidentiality of financial data. Consequently, Federated Learning (FL) has emerged as a promising solution, enabling the secure and private training of models across organizations. However, the practical implementation of FL is challenged by data heterogeneity among institutions, complicating model convergence. To address this issue, we propose FedFusion, which leverages the fusion of local and global models to harness the strengths of both, ensuring convergence even with heterogeneous data with total feature discrepancy. Our approach involves three distinct datasets with completely different feature sets assigned to separate federated clients. Prior to FL training, datasets are preprocessed to select significant features across three deep learning models. The Multilayer Perceptron (MLP), identified as the best-performing model, undergoes personalized training for each dataset. These trained MLP models serve as local models, while the main MLP architecture acts as the global model. FedFusion then adaptively trains all clients, optimizing fusion proportions. Experimental results demonstrate the approach’s superiority, achieving detection rates of 99.74%, 99.70%, and 96.61% for clients 1, 2, and 3, respectively. This highlights the effectiveness of FedFusion in addressing data heterogeneity challenges, thereby paving the way for more secure and efficient fraud detection systems in digital finance. |
| 書誌情報 |
en : IEEE Access
巻 12,
p. 136962-136978,
発行日 2024-09-19
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| 出版者 |
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出版者 |
IEEE |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2169-3536 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1109/ACCESS.2024.3464333 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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|
識別子タイプ |
URI |
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関連識別子 |
https://ieeexplore.ieee.org/abstract/document/10684281 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
$00A9 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
IPA |
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研究課題名 |
ICSCoE Core Human Resources Development Program |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
JP24K02916 |
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研究課題名 |
高信頼システム間連携のための仮想/現実空間連動ブロックチェーン基盤の研究開発 |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
JP24K03045 |
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研究課題名 |
データセントリックな信頼志向データ流通管理の研究 |
| 助成情報 |
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|
助成機関名 |
Ministry of Education, Culture, Sports, Science and Technology(MEXT), MEXT Scholarship |
