Data privacy and regulatory compliance are also crucial threats to the centralized machine learning practice in the digital financial environment. Federated Learning (FL) provides a referral-free solution that enables several financial units to collaborate in training machine learning models without sharing sensitive customer data. The paper investigates the architecture design, system issues, and performance enhancements of FL in distributed financial settings. By conducting a thorough analysis of communication restrictions, data heterogeneity, and security flaws, the paper identifies obstacles to the smooth implementation of FL. Other emerging technologies in the privacy-preserving methods, adaptive optimization algorithms, and hierarchical architectures relate specifically to the area of financial applications that are evaluated in the paper. Moreover, it presents new governance regimes and practical applications, underscoring the relevance of FL in fraud detection and credit rating assessment, as well as in investment approaches. The insights introduced not only demonstrate the viability of FL in the financial sphere but also outline a path towards the creation of safe, scalable, and regulatory-compliant AI ecosystems. Grounding technology in collaboration between institutions and legal frameworks, federated learning is one of the core infrastructures of the new era of safe, intelligent financial systems.
Saravanan Thirumazhisai Prabhagaran (Tue,) studied this question.