Academic Publications

Training federated learning (FL) at scale suffers from severe communication and heterogeneity constraints. These challenges are amplified in quantum federated learning (QFL), especially under non-IID data. We propose a model-driven QFL (mdQFL) framework that addresses communication overhead, scalability, and client drift through adaptive clustering and representative aggregation. The framework enables structured personalization and efficient update compression across rounds. mdQFL is the first QFL approach to jointly analyze training efficiency, personalization, and test generalization under heterogeneous conditions. Experiments across multiple datasets and quantum platforms show 50% communication reduction while maintaining or improving accuracy over standard QFL baselines. We provide convergence guarantees and communication complexity bounds to establish scalability and robustness.

As Quantum Federated Learning (QFL) scales toward distributed quantum networks, managing heterogeneous resources and communication bottlenecks becomes a critical challenge. This research proposes LLM-QFL, an adaptive network service management framework that leverages Large Language Models (LLMs) to optimize the operational efficiency of QFL systems. We introduce a federated distillation method in which locally fine-tuned LLMs serve as autonomous network agents. These agents adaptively manage service parameters by: i) dynamically adjusting local computation intensity (optimizer steps) based on loss gradients, ii) performing variance-aware client selection to minimize network-wide heterogeneity, and iii) implementing intelligent early stopping criteria to conserve bandwidth. By serving as an orchestration layer, LLM-QFL provides a synergy between LLMs and quantum networking. Our contributions include: i) Adaptive Performance and Efficiency: Reducing idle computation and significantly cutting communication overhead; ii) Theoretical Rigor: Convergence guarantees of O(1/T) for the adaptive management protocol; and iii) Scalable Deployment: Implementing PEFT (LoRA/QLoRA) for resource-constrained quantum service nodes.

This paper presents a comprehensive review and empirical analysis of Quantum Federated Learning (QFL), an emerging interdisciplinary field integrating quantum machine learning (QML) with federated learning. Driven by the growing capabilities of quantum computing and the various challenges in classical federated learning such as computational capacity, handling large-scale distributed data, etc., we examine the theoretical foundations, methodological developments, and practical challenges of QFL. Our work systematically examines the existing literature, categorizes existing QFL approaches, and assesses their applicability across diverse domains. Leveraging major quantum platforms including Qiskit, PennyLane and Tensor-Circuit, we conduct extensive experiments on both simulators and real IBM quantum hardware. We provide comprehensive literature review, extensive experimental analysis showcasing significant discrepancies such as between simulated and realhardware results, impact of heterogeneity, platform specific limitations with multiple ablation studies examining impact of noise, quantum classifier, simulators etc. We conclude with discussions on various insights that identify open research challenges and outline future directions for scalable and reliable QFL integration with next-generation technologies.

In this research, we are the first to examine the potential and practicality of achieving a quantum computing advantage within communication relay networks. Barrage Relay networks (BRNs) offer a low-latency and resilient network structure capable of preventing collisions through self-directed cooperation. We extend BRN by designing a multi-hop quantum teleportation-based relay network.

The integration of quantum machine learning into federated learning paradigms is poised to transform the future of technologies that depend on diverse machine learning methodologies. This research delves into Quantum Federated Learning (QFL), presenting an initial framework modeled on the Federated Averaging (FedAvg) algorithm, implemented via Qiskit. Despite its potential, QFL encounters critical challenges, including (i) susceptibility to a single point of failure, (ii) communication bottlenecks, and (iii) uncertainty in model convergence. Subsequently, we dive deeper into QFL and propose an innovative alternative to traditional server-based QFL. Our approach introduces a chained continuous QFL framework (ccQFL), which eliminates the need for a central server and the FedAvg method. In our framework, clients engage in a chained continuous training process, where they exchange models and collaboratively enhance each other's performance.

We present a novel decentralized and trustworthy Quantum Federated Learning (QFL) framework tailored for the emerging Metaverse. This virtual environment, enabling social interaction, gaming, and commerce, demands secure and transparent systems. By integrating blockchain, our QFL framework ensures integrity, resilience, and transparency. Comparative analysis with classical Federated Learning (CFL) highlights its practicality and advantages in distributed settings. New insights developed emphasize the importance of decentralized systems for the Metaverse's evolution, with a blockchain-based QFL application demonstrated in a hybrid model.

Advances in federated and quantum computing have improved data privacy and efficiency in distributed systems. Quantum Federated Learning (QFL), like its classical counterpart, Classic Federated Learning (CFL), struggles with challenges in heterogeneous environments. To address these, we propose wp-QFL, a weighted personalized approach with quantum federated averaging (qFedAvg), tackling non-IID data and local model drift. The code implementation is available at https://github.com/s222416822/wpQFL.

This work presents vQFL (vehicular Quantum Federated Learning), a new framework that leverages quantum machine learning techniques to tackle key privacy and security issues in autonomous vehicular networks. By integrating quantum federated learning with differential privacy and quantum key distribution (QKD), our quantum vanguard approach creates a multi-layered defense.

Quantum Federated Learning (QFL) promises to revolutionize distributed machine learning by combining computational power of quantum devices with collaborative training. Yet privacy of data and models remains critical. In this work, we propose a multi-layered privacy protocol using SVD, QKD, and AQGD.

We propose a quantum-secure federated learning (QFL) framework designed to safeguard distributed learning systems against quantum-enabled adversaries. We integrate QKD, Quantum Teleportation, Key Encapsulation Mechanisms (KEM), and Post-Quantum Cryptography (PQC).

Low Earth orbit (LEO) constellations violate core assumptions of standard FL. We present sat-QFL, a hierarchical, access aware quantum federated learning (QFL) framework partitioning satellites into primary ground-connected and secondary ISL-only roles.

Quantum Federated Learning (QFL) faces significant challenges due to statistical heterogeneity. To address this, we propose a personalized QFL algorithm based on hierarchical clustering, pQFL-HC, which employs clustering of clients based on model parameter similarity.

We propose orbital QFL, termed orb-QFL, a novel quantum-assisted Federated Learning framework tailored for Low Earth Orbit (LEO) satellite constellations operating without central server aggregation.

As the field of quantum computing progresses, traditional cryptographic algorithms such as RSA and ECDSA are becoming increasingly vulnerable to quantum-based attacks, underscoring the need for robust post-quantum security in critical systems like Federated Learning (FL) and Blockchain. In light of this, we propose a novel hybrid approach for blockchain-based FL (BFL) that integrates a stateless signature scheme, such as Dilithium or Falcon, with a stateful hash-based scheme like XMSS.

We develop a novel method by combining weighted personalization with quantum federated averaging to address impending challenges such as non-IID data distribution and client drift. The proposed weighted personalized Quantum Federated Learning (wpQFL) dynamically adapts to data heterogeneity.

We design a model of Post Quantum Cryptography (PQC) Quantum Federated Learning (QFL). We develop a proof of concept with a dynamic server selection and study convergence and security conditions.