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FedAugSec: A federated learning framework with data augmentation and selective encryption for financial fraud detection

computer-vision
data-mining-and-machine-learning
distributed-and-parallel-computing
security-and-privacy
neural-networks

Abstract

Federated learning (FL) has become a key solution for implementing privacy-preserving machine learning in the financial sector, enabling institutions to collaborate on model training without sharing sensitive data. Given the high compliance requirements for financial data, FL ensures that original data remains locally, reducing the risk of data leakage and compliance violations. However, traditional FL still faces numerous challenges in terms of limited client computing power and communication security. To address these issues, this paper proposes a more versatile and secure federated learning framework. To further enhance the system's privacy protection capabilities and model performance, we introduce a selective encryption mechanism to encrypt the transmission of sensitive parameters, balancing communication efficiency and security. We also incorporate data augmentation strategies to mitigate the impact of distribution differences on model accuracy. Extensive experiments verify the feasibility and effectiveness of the proposed method.
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