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 investing in R&D forQML.
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 investing in R&D forQML.
As federated learning (FL) keeps dominating the way in privacy-preserving machine learning (ML), notably in finance, its long-term sustainability is confronted with the approaching challenge of quantum computing. The introduction of quantum computing poses a challenge to traditional cryptographic technique on which FL relies RSA, elliptic curve cryptography (ECC), and some homomorphic encryption (HE) methods applied for encrypting model updates during training rendering this chapter to explore the interplay between the threat of quantum computing and the FL architectures, with emphasis on the implications this interaction introduces to cybersecurity and privacy in finance applications. Quantum computers are theoretically capable of solving hard math problems that have been regarded as computationally intractable for quantum computers. The well-known algorithms like Shor’s and Grover’s provides serious risk to the cryptographic procedures FL pipelines depends on. The finance sector, by its nature involving sensitive data and dependence on secure, distributed ML systems, is particularly vulnerable. The chapter begins by summarizing the fundamentals of FL and current encryption methods adapted to protect FL workflows. It then shifts towards the essence of quantum computing, emphasizing on the notable algorithms that threaten FL. We also explore potential attack points through which quantum computing could attack FL, e.g., model inversion attacks, poisoning attacks facilitated by quantum capabilities, and threats to secure aggregation. Post-quantum cryptography (PQC) is also presented as a potential counterattack, followed by a discussion of its use case in FL platforms. This chapter attempts to close the gap between FL and quantum computing threats to the financial sector. It is an introduction to practitioners and researchers to revisit examining security assumptions in FL and move towards the quantum-augmented cyber threat model.
Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of the QFL research. This work explores key developments and integration strategies, along with the impact of QC on FL, keeping a sharp focus on hybrid quantum-classical approaches. The paper offers an in-depth understanding of how the strengths of QC, such as gradient hiding, state entanglement, quantum key distribution, quantum security, and quantum-enhanced differential privacy, have been integrated into FL to ensure the privacy of participants in an enhanced, fast, and secure framework. Finally, this study proposes potential future directions to address the identified research gaps and challenges, aiming to inspire faster and more secure QFL models for practical use.
Quantum Federated Learning (QFL) recently becomes a promising approach with the potential to revolutionize Machine Learning (ML). It merges the established strengths of classical Federated Learning (FL) with the exceptional parallel mechanism and exponential speed enhancements characteristic of quantum computing. While this innovative fusion has garnered considerable attention, a notable gap in current research is the tendency to consider traditional FL and its quantum-enhanced counterpart, QFL, in isolation. This approach often overlooks the critical role of Quantum Machine Learning (QML) in effectively bridging these two domains. Recognizing this, there emerges a pressing need for a comprehensive survey that encompasses the entire spectrum of FL paradigms, from foundational FL concepts to the cutting-edge developments in QFL. Our survey aims to address this need by providing an in-depth exploration of the various facets of FL paradigms, ultimately leading to a thorough understanding of Quantum Federated Learning. We start by emphasizing the driving factors and prevalent research topics related to FL. To develop a more efficient, robust, and precise computing paradigm, we investigate the current solutions that address the concerns of heterogeneity, privacy, security, and evaluation in FL. After that, we explore the possibility of improving the computational efficiency of ML methods by leveraging the computational capabilities of quantum computers. In particular, we discuss the frameworks, evaluation, and applications for QML. Following that, we detail the integration of quantum computing technologies with standard FL, aiming to bolster computational performance and mitigate security and privacy risks. The potential solutions to improve the efficiency (i.e., slimming mechanism) and respect the privacy and security (i.e., quantum key distribution) for QFL are explored. Finally, we outline some critical future directions towards unlocking the full potential of QFL in distributed machine learning.
Collaborative threat intelligence via federated learning (FL) faces critical risks from quantum computing, which can compromise classical encryption methods. This study proposes a quantum-secure FL framework using post-quantum cryptography (PQC) to protect cross-organizational data sharing. We expose vulnerabilities in traditional FL through simulated quantum attacks on RSA encrypted gradients and introduce a hybrid architecture integrating NIST-standardized algorithms CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication. Testing on APT attack datasets demonstrated 97.6% threat detection accuracy with minimal latency overhead (18.7%), validating real-world viability. A healthcare consortium case study confirmed secure ransomware indicator sharing without breaching privacy regulations. The work highlights the urgency of quantum ready defenses and provides technical guidelines for deploying PQC in FL systems, alongside policy recommendations for standardizing quantum resilience in threat-sharing networks.
The financial sector faces escalating cybersecurity threats, exacerbated by the potential emergence of quantum computers capable of compromising widely used classical cryptographic methods. The paper includes an AI-enhanced general review on the use of quantum cryptography to secure financial transactions. Using machine learning methods to assess the principles, potential, and constraints to Quantum Key Distribution (QKD) and PostQuantum Cryptography (PQC), the review is an improvement of the existing view of the current state of quantumsafe technologies. The opportunities and challenges in adopting these technologies in the key financial services, such as banking, digital payments, and blockchain, are analyzed. It has been identified that a hybrid approach that utilizes both QKD to perform secure key exchange and PQC to protect data over the long term can be considered an effective strategy. The review ends by identifying the main future research opportunities and the necessity of standardized, cost-efficient, and scalable quantum security solutions, guided by AI-informed insights, to protect the financial sector against future quantum attacks.
Abstract : The financial sector faces escalating cybersecurity threats, exacerbated by the potential emergence of quantum computers capable of compromising widely used classical cryptographic methods. The paper includes an AI-enhanced general review on the use of quantum cryptography to secure financial transactions. Using machine learning methods to assess the principles, potential, and constraints to Quantum Key Distribution (QKD) and PostQuantum Cryptography (PQC), the review is an improvement of the existing view of the current state of quantumsafe technologies. The opportunities and challenges in adopting these technologies in the key financial services, such as banking, digital payments, and blockchain, are analyzed. It has been identified that a hybrid approach that utilizes both QKD to perform secure key exchange and PQC to protect data over the long term can be considered an effective strategy. The review ends by identifying the main future research opportunities and the necessity of standardized, cost-efficient, and scalable quantum security solutions, guided by AI-informed insights, to protect the financial sector against future quantum attacks.