Sandeep Dasari, Rajesh Kaluri*
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2035-2051, 2024, DOI:10.32604/cmes.2024.049152
- 20 May 2024
Abstract The increasing data pool in finance sectors forces machine learning (ML) to step into new complications. Banking data has significant financial implications and is confidential. Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages. As a result, this study employs federated learning (FL) using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model. However, diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy. To address this issue, the… More >
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