Federated learning and Quantum Computing for cybersecurity in the banking Sector: a systematic review
Abstract
The modern banking sector faces growingcybersecurity challenges, demanding collaborative yet privacypreservingdefense mechanisms. Federated Learning (FL) hasemerged as a key paradigm for addressing these needs, but theadvent of quantum computing threatens its long-term viability.This development introduces a critical duality: the existentialthreat of quantum capabilities to break classical cryptography andthe transformative opportunities offered by Quantum MachineLearning (QML). While research has addressed these aspectsseparately, a unified analysis that unites them is lacking. Thispaper presents the first comprehensive systematic review mappingthis dualistic landscape. Following PRISMA guidelines, the reviewsynthesizes the current state of research and evaluates thechallenges and opportunities at the intersection of FL, quantumcomputing, and financial cybersecurity. The findings reveala strong consensus on the urgent need to integrate Post-QuantumCryptography (PQC) to secure FL frameworks against futurethreats. Simultaneously, a long-term vision is emerging to leverageQML to enhance threat detection capabilities beyond classicalboundaries. A significant trend toward integrated, layeredarchitectures that combine FL, blockchain, and quantumtechnologies is also identified. This review concludes that theevolution of FL in banking is inextricably linked to quantumadvances, demanding a two-pronged proactive strategy: mitigatingimmediate risks with PQC while strategically…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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