{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:20:39Z","timestamp":1776079239778,"version":"3.50.1"},"reference-count":0,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T00:00:00Z","timestamp":1717286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mastercard Research Funds","award":["DMS2022448"],"award-info":[{"award-number":["DMS2022448"]}]},{"name":"Mastercard Research Funds","award":["CCF-0939370"],"award-info":[{"award-number":["CCF-0939370"]}]},{"name":"Meta Gift Funds","award":["DMS2022448"],"award-info":[{"award-number":["DMS2022448"]}]},{"name":"Meta Gift Funds","award":["CCF-0939370"],"award-info":[{"award-number":["CCF-0939370"]}]},{"name":"NSF","award":["DMS2022448"],"award-info":[{"award-number":["DMS2022448"]}]},{"name":"NSF","award":["CCF-0939370"],"award-info":[{"award-number":["CCF-0939370"]}]},{"name":"Data Science and Cybersecurity Center","award":["DMS2022448"],"award-info":[{"award-number":["DMS2022448"]}]},{"name":"Data Science and Cybersecurity Center","award":["CCF-0939370"],"award-info":[{"award-number":["CCF-0939370"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment employs FL to establish a global learning model on the cloud server, which transmits initial parameters to individual local learning models on fog nodes. With three banks (fog nodes) involved, each bank trains its learning model locally, ensuring data privacy, and subsequently sends back updated parameters to the global learning model. Through the integration of FL and blockchain, our system ensures privacy preservation and data protection. We utilize three machine learning and deep neural network learning algorithms, RF, CNN, and LSTM, alongside deep optimization techniques such as ADAM, SGD, and MSGD. The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy.<\/jats:p>","DOI":"10.3390\/fi16060196","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T03:51:45Z","timestamp":1717386705000},"page":"196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems"],"prefix":"10.3390","volume":"16","author":[{"given":"Tahani","family":"Baabdullah","sequence":"first","affiliation":[{"name":"Data Science and Cybersecurity Center (DSC2), Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA"},{"name":"Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6981-3171","authenticated-orcid":false,"given":"Amani","family":"Alzahrani","sequence":"additional","affiliation":[{"name":"Data Science and Cybersecurity Center (DSC2), Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA"},{"name":"Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3638-3464","authenticated-orcid":false,"given":"Danda B.","family":"Rawat","sequence":"additional","affiliation":[{"name":"Data Science and Cybersecurity Center (DSC2), Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA"},{"name":"Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA"}]},{"given":"Chunmei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,2]]},"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/6\/196\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:52:32Z","timestamp":1760107952000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/6\/196"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,2]]},"references-count":0,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["fi16060196"],"URL":"https:\/\/doi.org\/10.3390\/fi16060196","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,2]]}}}