Implementation of the ML-KEM Protocol for Securing Parameter Exchange in FedAvg-Based Federated Learning Architecture Against Quantum Computing Threats
Ariq Arsalan, Eko Hari Rachmawanto, Christy Atika Sari
Federated Learning (FL) enables collaborative machine learning by aggregating local models from decentralized clients without sharing raw data. However, the parameter exchange process in standard FL architectures is vulnerable to emerging quantum computing threats. Specifically, Shor's algorithm, executable on large-scale quantum computers, can break conventional asymmetric cryptography such as RSA and ECDH in polynomial time, thereby threatening the security of FL systems that rely on these traditional public-key infrastructures for parameter exchange. This study addresses this vulnerability by implementing the recently standardized post-quantum cryptographic protocol, ML-KEM (Module-Lattice-Based Key-Encapsulation Mechanism), within a FedAvg-based FL architecture. The integration is designed to secure the parameter exchange pipeline without compromising the neural network's performance. Experimental results on a simulated environment utilizing the MNIST dataset demonstrate that the ML-KEM integration preserves 100% of the global model accuracy. Furthermore, the cryptographic latency overhead introduced by encapsulation and decapsulation remains highly efficient, proving its potential as a robust security layer. While current evaluations focus on small-scale deployments, the proposed architecture establishes a foundational post-quantum security framework for future privacy-preserving distributed learning systems.
Federated Learning (FL) enables collaborative machine learning by aggregating local models from decentralized clients without sharing raw data. However, the parameter exchange process in standard FL architectures is vulnerable to emerging quantum computing threats. Specifically, Shor's algorithm, executable on large-scale quantum computers, can break conventional asymmetric cryptography such as RSA and ECDH in polynomial time, thereby threatening the security of FL systems that rely on these traditional public-key infrastructures for parameter exchange. This study addresses this vulnerability by implementing the recently standardized post-quantum cryptographic protocol, ML-KEM (Module-Lattice-Based Key-Encapsulation Mechanism), within a FedAvg-based FL architecture. The integration is designed to secure the parameter exchange pipeline without compromising the neural network's performance. Experimental results on a simulated environment utilizing the MNIST dataset demonstrate that the ML-KEM integration preserves 100% of the global model accuracy. Furthermore, the cryptographic latency overhead introduced by encapsulation and decapsulation remains highly efficient, proving its potential as a robust security layer. While current evaluations focus on small-scale deployments, the proposed architecture establishes a foundational post-quantum security framework for future privacy-preserving distributed learning systems.
Federated Learning (FL) enables collaborative machine learning without centralizing raw data. However, the impending threat of cryptographically-relevant quantum computers exposes FL's reliance on classical public-key cryptography (e.g., RSA, ECC) to Harvest-Now-Decrypt-Later (HNDL) attacks. We present PQ-FL, the first open federated learning control plane secured end-to-end using the finalized NIST Post-Quantum Cryptography (PQC) standards. Our architecture integrates ML-DSA-87 (FIPS 204) for transport-layer identity authentication (mTLS) and ML-KEM-1024 (FIPS 203) for application-layer model payload encryption. To overcome the lack of comprehensive PQC support in standard Python libraries (BoringSSL), we introduce a novel OQS-stunnel sidecar pattern that proxies edge client traffic. We evaluate the system on a CUDA-accelerated edge cluster training a convolutional neural network. Empirical results confirm successful multi-round aggregation using FedAvg, Krum, and Trimmed-Mean strategies alongside differential privacy, achieving seamless PQC key encapsulation (1568-byte ciphertexts) and verifiable asynchronous global model streaming.
Industrial innovations, driven by technologies such as the Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI), have revolutionized various operational domains, including supply chain management, equipment monitoring, predictive maintenance, and quality control. Deep Learning (DL) has become instrumental in addressing complex, data-driven challenges within industrial settings, relying on large datasets collected from heterogeneous sources. However, centralizing these datasets on cloud platforms poses significant privacy and security risks. Federated Learning (FL) offers a promising solution by enabling distributed model training across multiple nodes while preventing the exchange of raw data. Despite its advantages, FL introduces new vulnerabilities, especially related to the security of communication channels between participating entities. Traditional cryptographic mechanisms, such as digital signatures and model encryption, can mitigate these risks, but the emergence of quantum computing threatens the robustness of conventional solutions. This work explores the integration of Post-Quantum Cryptography (PQC) into FL to enhance security without incurring significant performance degradation. A modular FL architecture with three security levels is proposed: (i) an unprotected baseline; (ii) authenticated communication using ML-DSA digital signatures; and (iii) full protection combining ML-DSA and ML-KEM-512-based key encapsulation. Implemented within the Flower framework, the architecture is evaluated under simulated MitM attacks. Experimental results demonstrate that PQC-enhanced schemes effectively mitigate quantum-resistant threats while maintaining acceptable computational overhead, thereby ensuring model integrity and data confidentiality.
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.
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 intensive development of smart city cyber-infrastructure has aggravated the security issues because of the heterogeneity of devices, the volume of constantly changing data, and the lack of confidentiality in the long term. Traditional public-key cryptographic systems like RSA and ECC are becoming susceptible to quantum-powered attacks and are inappropriate to use in smart city applications in the future. Despite their quantum resistance, post-quantum cryptographic (PQC) schemes are not widely used in practice due to high computational cost, difficulty in key management, and inflexibility to dynamic cyber-threats. Furthermore, the current security systems do not tend to be decentralized, but the sources of intelligence are usually centralized which creates issues of privacy and scalability. This paper mitigates these shortages by suggesting a safe post-quantum architecture that incorporates an adjustable federated learning architecture and a hybridized lattice-based Key Encapsulation Mechanism to smart city cyber-infrastructure. The KEM based on lattice provides resistant to quantum key exchange, whereas federated learning provides decentralized threat intelligence without sensitive local information disclosure. An adaptive aggregation approach dynamically scales model updates based on node reliability and threat intensity, improving resilience to poisoning and inference attacks. Benchmark post-quantum parameters and public intrusion datasets, such as UNSW-NB15 and CICIDS2017 are tested in the framework in a simulated smart city setting. The experimental findings indicate a maximum common exchange latency reduction of 21 percent, versus standalone lattice schemes, and a threat detection rate of 97.8 percent with stable convergence. The results have shown the efficiency of integrating post-quantum cryptography with adaptive federated intelligence in ensuring the security of the next generation smart city infrastructures.
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.