%0 Journal Article %1 dashfederated %A Dash, Bibhu %D 2022 %E Inukollu, Venkata N %J International Journal of Software Engineering & Applications (IJSEA) %K FinTech %N 4 %P 1-13 %T Federated Learning for Privacy-Preserving: A Review of PII Data Analysis in Fintech %U https://aircconline.com/ijsea/V13N4/13422ijsea01.pdf %V 13 %X There has been tremendous growth in the field of AI and machine learning. The developments across these fields have resulted in a considerable increase in other FinTech fields. Cyber security has been described as an essential part of the developments associated with technology. Increased cyber security ensures that people remain protected, and that data remains safe. New methods have been integrated into developing AI that achieves cyber security. The data analysis capabilities of AI and its cyber security functions have ensured that privacy has increased significantly. The ethical concept associated with data privacy has also been advocated across most FinTech regulations. These concepts and considerations have all been engaged with the need to achieve the required ethical requirements. The concept of federated learning is a recently developed measure that achieves the abovementioned concept. It ensured the development of AI and machine learning while keeping privacy in data analysis. The research paper effectively describes the issue of federated learning for confidentiality. It describes the overall process associated with its development and some of the contributions it has achieved.

@article{dashfederated, abstract = {There has been tremendous growth in the field of AI and machine learning. The developments across these fields have resulted in a considerable increase in other FinTech fields. Cyber security has been described as an essential part of the developments associated with technology. Increased cyber security ensures that people remain protected, and that data remains safe. New methods have been integrated into developing AI that achieves cyber security. The data analysis capabilities of AI and its cyber security functions have ensured that privacy has increased significantly. The ethical concept associated with data privacy has also been advocated across most FinTech regulations. These concepts and considerations have all been engaged with the need to achieve the required ethical requirements. The concept of federated learning is a recently developed measure that achieves the abovementioned concept. It ensured the development of AI and machine learning while keeping privacy in data analysis. The research paper effectively describes the issue of federated learning for confidentiality. It describes the overall process associated with its development and some of the contributions it has achieved. }, added-at = {2023-08-18T11:07:52.000+0200}, author = {Dash, Bibhu}, biburl = {https://www.bibsonomy.org/bibtex/27ab41f252b598ddc6f31d9f259735d8e/janurichie_123}, editor = {Inukollu, Venkata N}, interhash = {8b47b96c30788eea45fc8f62bf57e31b}, intrahash = {7ab41f252b598ddc6f31d9f259735d8e}, issn = {0975-9018}, journal = {International Journal of Software Engineering & Applications (IJSEA)}, keywords = {FinTech}, language = {English}, month = {July}, number = 4, pages = {1-13}, timestamp = {2023-08-18T11:07:52.000+0200}, title = {Federated Learning for Privacy-Preserving: A Review of PII Data Analysis in Fintech}, url = {https://aircconline.com/ijsea/V13N4/13422ijsea01.pdf}, volume = 13, year = 2022 }