Smart Finance Innovations with Federated Learning and Blockchain Integration

By combining blockchain technology with federated learning, this study introduces a novel method of smart finance. The proposed method is designed to resolve the substantial challenges associated with data privacy, security, and operational efficiency that are prevalent in conventional banking systems. Federated learning enables numerous financial institutions to develop machine learning models that enhance privacy by cooperating rather than exchanging raw data. Furthermore, blockchain technology ensures the integrity and transparency of data by creating an immutable and distributed ledger. The federated learning process is automated by smart contracts, which ensures the safety and efficiency of participant participation. The system’s efficacy in enhancing model accuracy, operational efficiency, and trustworthiness, while simultaneously ensuring robust data privacy and security, is demonstrated by experiments. This integration is a substantial development in financial technology, as it provides a secure, efficient, and intelligent platform for the evaluation and decision-making process based on financial data. The model performed 95% accuracy.

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Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/incet64471.2025.11140005
Citations
6
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
19.48
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article

Smart Finance Innovations with Federated Learning and Blockchain Integration

G. Manikandan, S.N. Gomathi Balan, Sumit Pundir, K. Thilagavathi et al.
6 citations
Blockchain Technology Applications and Security
19.48
article

Smart Finance Innovations with Federated Learning and Blockchain Integration

G. Manikandan, S.N. Gomathi Balan, Sumit Pundir, K. Thilagavathi, R Rakesh, P. Raman
article en
6 citations

Abstract

By combining blockchain technology with federated learning, this study introduces a novel method of smart finance. The proposed method is designed to resolve the substantial challenges associated with data privacy, security, and operational efficiency that are prevalent in conventional banking systems. Federated learning enables numerous financial institutions to develop machine learning models that enhance privacy by cooperating rather than exchanging raw data. Furthermore, blockchain technology ensures the integrity and transparency of data by creating an immutable and distributed ledger. The federated learning process is automated by smart contracts, which ensures the safety and efficiency of participant participation. The system’s efficacy in enhancing model accuracy, operational efficiency, and trustworthiness, while simultaneously ensuring robust data privacy and security, is demonstrated by experiments. This integration is a substantial development in financial technology, as it provides a secure, efficient, and intelligent platform for the evaluation and decision-making process based on financial data. The model performed 95% accuracy.

Central University of Tamil Nadu (IN), Chennai Mathematical Institute (IN), Periyar Maniammai Institute of Science & Technology (IN), Graphic Era University (IN), Saveetha University (IN)
Openalex Percentile: Top 1%
Blockchain Technology Applications and Security
19.48
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