The rapid digital transformation of financial services has significantly increased the volume and complexity of electronic transactions, making automated fraud detection an essential component of modern banking systems. Machine learning (ML) techniques have demonstrated remarkable success in identifying fraudulent activities by learning complex transaction patterns from historical financial data. However, conventional centralized machine learning approaches require organizations to consolidate sensitive customer information into centralized repositories, increasing the risk of privacy breaches, unauthorized access, and regulatory non-compliance with frameworks such as the General Data Protection Regulation (GDPR) and other financial data protection standards [2, 4].

 

Federated Learning (FL) has emerged as a promising distributed learning paradigm that enables multiple organizations to collaboratively train machine learning models without exchanging raw data [1,4]. Although FL significantly improves data privacy, recent studies have demonstrated that federated learning remains vulnerable to adversarial attacks, including model poisoning, data poisoning, backdoor attacks, membership inference, and gradient inversion attacks, all of which can compromise model integrity and reveal confidential information [7,13].

 

This paper proposes SecureFedShield, a privacy-preserving federated learning framework designed for secure financial fraud detection in adversarial environments. The proposed framework integrates adaptive privacy protection, trust-aware client evaluation, adversarial update detection, and robust model aggregation into a unified architecture. By combining these complementary mechanisms, SecureFedShield aims to improve resilience against malicious participants while preserving high fraud detection accuracy. The framework is intended to be evaluated using publicly available financial fraud datasets and compared with state-of-the-art federated learning aggregation methods. The proposed architecture provides a practical foundation for deploying secure collaborative machine learning in privacy-sensitive financial institutions.