Federated Learning and Quantum Computing for Cybersecurity in the Banking Sector: A Systematic Review
Abstract
The modern banking sector faces growing cybersecurity challenges, demanding collaborative yet privacy-preserving defense mechanisms. Federated Learning (FL) has emerged as a key paradigm for addressing these needs, but the advent of quantum computing threatens its long-term viability. This development introduces a critical duality: the existential threat of quantum capabilities to break classical cryptography and the transformative opportunities offered by Quantum Machine Learning (QML). While research has addressed these aspects separately, a unified analysis that unites them is lacking. This paper presents the first comprehensive systematic review mapping this dualistic landscape. Following PRISMA guidelines, the review synthesizes the current state of research and evaluates the challenges and opportunities at the intersection of FL, quantum computing, and financial cybersecurity. The findings reveal a strong consensus on the urgent need to integrate Post-Quantum Cryptography (PQC) to secure FL frameworks against future threats. Simultaneously, a long-term vision is emerging to leverage QML to enhance threat detection capabilities beyond classical boundaries. A significant trend toward integrated, layered architectures that combine FL, blockchain, and quantum technologies is also identified. This review concludes that the evolution of FL in banking is inextricably linked to quantum advances, demanding a two-pronged proactive strategy: mitigating immediate risks with PQC while strategically investing in R&D for QML.
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