Machine learning models in online environments are vulnerable to various adversarial attacks, such as those from generative adversarial networks (GANs) attacks and more powerful attacks based on recent advances in quantum computing. Federated Learning (FL), a modern privacy preserving distributed machine learning technique, also faces modern data management concerns related to quantum computing. Limiting adversarial learning through quantum mechanics lends itself as an effective security mechanism. This paper introduces a novel quantumencrypted FL framework that integrates randomly-generated quantum noise into an FL environment to improve security against adversarial learning algorithms. These algorithms can conduct reconstruction attacks, which increase privacy leakage through adversarially generated data. Specifically, GAN attacks are effective at creating data samples that resemble training data. Traditional computing techniques can be improved by appending random noise generated by quantum computers as a quantum verification method. The proposed hybrid-quantum encryption approach can fortify a network against generative reconstruction attacks by adding quantum noise to each participant's training data samples. To evaluate the effectiveness of the proposed quantum encryption techniques for protecting FL against GANs, we encrypt the MNIST dataset with quantum-generated keys and then distribute the data in a privacy-preserving manner.
Machine learning models in online environments are vulnerable to various adversarial attacks, such as those from generative adversarial networks (GANs) attacks and more powerful attacks based on recent advances in quantum computing. Federated Learning (FL), a modern privacy preserving distributed machine learning technique, also faces modern data management concerns related to quantum computing. Limiting adversarial learning through quantum mechanics lends itself as an effective security mechanism. This paper introduces a novel quantumencrypted FL framework that integrates randomly-generated quantum noise into an FL environment to improve security against adversarial learning algorithms. These algorithms can conduct reconstruction attacks, which increase privacy leakage through adversarially generated data. Specifically, GAN attacks are effective at creating data samples that resemble training data. Traditional computing techniques can be improved by appending random noise generated by quantum computers as a quantum verification method. The proposed hybrid-quantum encryption approach can fortify a network against generative reconstruction attacks by adding quantum noise to each participant's training data samples. To evaluate the effectiveness of the proposed quantum encryption techniques for protecting FL against GANs, we encrypt the MNIST dataset with quantum-generated keys and then distribute the data in a privacy-preserving manner.
Abstract Federated learning is a decentralized machine learning approach where multiple participants collaboratively train machine learning models. With the development of quantum computing, there has been significant potential in the integration of quantum computing and federated learning. However, existing research has demonstrated that, similar to classical federated learning models, quantum federated learning models also face various security threats and privacy leakage issues. This paper proposes a quantum federated learning model based on quantum noise. Adding quantum noise to the model not only addresses privacy leakage, but also enhances the model robustness, effectively resists adversarial attacks. Specifically, extensive numerical simulations are conducted using various datasets to evaluate the effectiveness of the proposed method. The results reveal a more pronounced variation in robust training in high-dimensional datasets compared to low-dimensional datasets. Furthermore, the impact of noise intensity on model robustness is explored. Experimental demonstrate that a small amount of quantum noise does not have a significant impact on accuracy, and as the noise increases, the robustness of the model also improves. Finally, three different types of quantum noise were used for robustness testing in the paper to analyze the impact of quantum noise on the robustness of quantum machine learning models. The abundant experimental results have verified that the noise can improve the security of distributed quantum machine learning.
The exponential expansion of data-driven technologies underscores the critical need for robust privacy safeguards. This study introduces an innovative privacy-preserving framework that combines Differential Privacy (DP) and Federated Learning (FL) with the cutting-edge capability of quantum computing. Leveraging Quantum Random Number Generators (QRNGs), the framework incorporates truly random quantum-generated noise into the differential privacy mechanism, substantially enhancing the security of gradient updates within machine learning models. This noise is specifically added to the gradient updates during the optimization process, using various optimizers. Quantum mechanics' inherent randomness ensures unpredictability, fortifying defenses against sophisticated attacks. This research signifies a significant step towards the development of secure and ethical AI technologies. By integrating quantum computing's random number generation capabilities into privacy-preserving mechanisms, the research paves the way for enhanced data security across various sectors. The findings indicate a promising path for further investigation and experimentation, with potential applications in fields demanding high-stakes data protection. This fusion of quantum computing and privacy-preserving techniques not only enhances the confidentiality of sensitive information but also sets a new standard for privacy in the age of data-driven innovation.
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing, addressing privacy concerns. However, traditional FL is vulnerable to attacks targeting model updates, potentially compromising data privacy. This paper proposes a novel decentralized federated learning framework that integrates quantum key distribution (QKD) to establish secure keys for data aggregation. The core claim is that QKD significantly enhances the security and efficiency of FL. The proposed system utilizes QKD to generate and distribute shared keys between participating devices, enabling secure aggregation of model updates while mitigating the risk of individual data exposure. This approach directly addresses the security vulnerabilities inherent in standard FL. The framework's architecture relies on a decentralized network topology, further bolstering resilience against single points of failure. Mathematical formulations detailing key aspects of the system, including key distribution protocols, aggregation processes, and security analysis, are presented. This work represents a significant advancement in secure FL by combining the strengths of two cutting-edge technologies: FL and QKD.
The rapid expansion of IoT ecosystems has increased concerns regarding data privacy, security, and model integrity, particularly in environments vulnerable to sophisticated adversarial and quantum-enabled attacks. Traditional cryptographic and Federated Learning (FL) methods cannot keep up with the demands for confidentiality and robustness in vast, diverse networks. Therefore, this study presents a Quantum-Inspired Cryptographic Framework that uses Quantum Neural Networks, Post-Quantum Cryptography, and secure Federated Learning to enhance intrusion detection systems and provide quantum resilient communication. The methodology uses the UNSW-NB15 dataset, which has been cleaned, encoded, and normalized with reduced features balanced by SMOTE before splitting into 70 percent training and 30 percent testing. A QNN-based FL model can then be trained while Kyber-512 and NTRUEncrypt secure all updates to the model, as well as node communications, against both classical and quantum threats. The experimental results show that the proposed model significantly outperforms classical FL frameworks, achieving an accuracy of 98.1%, precision of 97.5%, recall of 97.9%, and F1-score of 97.7%, even under very challenging adversarial conditions. QICF is therefore a robust, privacy-preserving, and attack-resilient solution for next-generation large-scale IoT networks.
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.
In recent years, the growing demand for secure, efficient, and scalable machine learning has highlighted significant challenges in traditional Federated Learning (FL) systems. These challenges include computational inefficiencies in handling large-scale, high-dimensional data, privacy concerns, and the limitations of classical hardware in solving complex optimization problems. Additionally, the increasing complexity of data and the need for more robust privacy-preserving methods have pushed the boundaries of classical FL. To address these issues, this paper presents a detailed framework for Quantum Federated Learning (QFL), where quantum computing and FL converge to enable distributed quantum model training with enhanced privacy preservation. The system is modeled using local quantum models on clients and a global aggregation strategy at the server. We develop quantum gradient-based optimization with quantum cross-entropy loss, privacy preservation through encryption, and robustness to quantum noise. A set of theorems is provided to prove the correctness and efficiency of the model, supported by rigorous mathematical formulations. We used IBM Qiskit for the simulation and compared our proposed QFL with classical FL. The results demonstrate that our QFL outperformed classical FL in terms of accuracy, precision, recall, and F1 score.