**Abstract:** Current drone swarm-based geohazard mapping systems are limited by computational bottlenecks during data processing and the lack of robust, real-time coordination in dynamically changing environments. This paper presents a novel approach, Hyper-Efficient Quantum-Enhanced Federated Learning (HQE-FL), that leverages federated learning techniques enhanced by quantum-inspired optimization and edge-AI processing to significantly accelerate data aggregation, improve swarm coordination efficiency, and enhance accuracy in dynamic geohazard mapping applications. HQE-FL allows individual drones to perform localized computations and share only model updates, minimizing data transmission and maximizing processing speed while maintaining data privacy. This framework enables real-time adaptation to changing geohazard conditions, improving the timeliness and reliability of risk assessments and mitigation strategies, achieving a 10x performance increase over existing systems.
Abstract: Current drone swarm-based geohazard mapping systems are limited by computational bottlenecks during data processing and the lack of robust, real-time coordination in dynamically changing environments. This paper presents a novel approach, Hyper-Efficient Quantum-Enhanced Federated Learning (HQE-FL), that leverages federated learning techniques enhanced by quantum-inspired optimization and edge-AI processing to significantly accelerate data aggregation, improve swarm coordination efficiency, and enhance accuracy in dynamic geohazard mapping applications. HQE-FL allows individual drones to perform localized computations and share only model updates, minimizing data transmission and maximizing processing speed while maintaining data privacy. This framework enables real-time adaptation to changing geohazard conditions, improving the timeliness and reliability of risk assessments and mitigation strategies, achieving a 10x performance increase over existing systems.
The United Nations Sustainable Development Goals (SDGs) demand intelligent, inclusive, and ethically driven solutions. Artificial Intelligence (AI) is a disruptive technology in this space, but its application must be efficient and privacy-preserving. This review explores the integration of Quantum Computing (QC) with Federated Learning (FL) into a novel architecture: Quantum-Federated Learning (QFL). QFL facilitates decentralized, secure model training across clients like hospitals or smart grids without centralizing sensitive data. While FL alone faces computational limitations, QC addresses these through parallelism and high-speed optimization. The proposed QFL framework enables edge nodes to train quantum-enhanced models locally and share only encrypted updates with a central quantum aggregation server. We detail the QFL architecture—comprising quantum-enabled clients, a secure communication layer leveraging quantum cryptography, and a quantum server—and its workflow. The review critically analyzes how QFL can develop applications supporting specific SDGs: SDG 3 (privacy-preserving collaborative healthcare diagnostics), SDG 7 (optimized demand forecasting in decentralized smart grids), SDG 9 (secure predictive maintenance in industry), and SDG 13 (collaborative climate modeling without infringing data sovereignty). Major challenges such as hardware limitations, standardization, and quantum-security issues are discussed. The paper concludes that QFL represents a strategic milestone towards creating AI systems that are not only intelligent and high-performing but also ethical, reliable, and sustainable.
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.
In this era of automation, drones have considered to be a key element with its wide applicability in various situations. Additionally, the efficiency of drones can be further improved via collaborated learning techniques such as federated learning (FL). However, the drone-based FL requires a significant amount of communications because the drones share their FL data over wireless channels. Therefore, the unstable channel condition of wireless transmission introduces the low success probability in large-scale parameter transmission. According to quantum computing (QC) advantages, this problem can be tackled due to less parameter utilization in quantum neural network (QNN) training. Therefore, a novel QC-based FL known as theadaptive quantum federated learning (AQFL)is proposed for drone license plate recognition in autonomous surveillance applications. In our proposed AQFL, adaptive QNNs (AQNNs) are used as local models which can control the depths of QNNs by adding measurement computations in the middles of QNN architectures. Thus, our proposed AQFL is robust even in training data non-iidness and large local model variances while maintaining sufficient learning performance. Finally, the evaluation results verify that our proposed AQFL achieves the desired performance improvements.
As real-time data processing becomes central to industries like retail, e-commerce, and entertainment, ensuring both computational efficiency and data privacy is increasingly vital. This study proposes a novel hybrid framework that integrates quantum-enhanced transformers within a Federated Learning (FL) system to perform real-time consumer sentiment analysis. While FL ensures data privacy by decentralizing model training across edge devices, it struggles with issues like high communication overhead and slow convergence in non-IID environments. On the other hand, quantum-enhanced models offer computational acceleration but often lack sufficient privacy safeguards, especially during quantum circuit operations. This research addresses these challenges by developing a quantumenhanced transformer optimized for sentiment analysis and embedding it into a privacy-preserving FL architecture. Advanced encryption techniques such as differential privacy and quantum homomorphic encryption are incorporated to mitigate data leakage risks. The study adopts a four-phase methodology: model development using quantum simulation platforms (e.g., IBM Qiskit), FL integration using FedAvg protocols, system validation through benchmarking against classical models, and real-time deployment simulations across industry-specific datasets. Results show that the proposed system achieves superior performance in latency, model accuracy, and privacy metrics compared to traditional FL models. Despite challenges related to quantum noise and heterogeneity in client data, the system demonstrates robustness and scalability under real-time conditions. This research contributes to the field by providing a viable, secure, and scalable solution for deploying quantum-AI in decentralized environments. It opens pathways for future research in hybrid quantum-classical architectures and domain-specific AI applications that require enhanced privacy and speed.
The article tackled the challenge of real-time data processing in dynamic, large-scale systems by examining machine learning strategies enhanced through quantum computing. As sectors such as smart cities and autonomous vehicles generated high-velocity, complex data, conventional algorithms faced limitations in adaptability and efficiency. This study systematically reviewed advanced quantum methodologies including quantum support vector machines, quantum particle swarm optimization, and quantum-enhanced gradient boosting and highlighted performance improvements over classical models. By leveraging specialized hardware and frameworks like GPUs and federated learning, these quantum-enhanced models achieved superior accuracy, scalability, and computational efficiency. Key stages in the proposed framework, such as data preprocessing and model adaptation, maximized efficiency, enabling real-time adaptability in dynamically changing environments. Findings demonstrated that integrating quantum techniques addressed significant computational challenges, showing strong potential for applications requiring rapid data response, minimal latency, and scalability. This research underscores the transformative impact of quantum-enhanced machine learning, offering robust solutions that could reshape complex, data-intensive industries dependent on real-time processing, such as healthcare, transportation, and finance. Future advancements in quantum computing could further solidify machine learning’s role in managing the demands of an increasingly data-driven world.
Federated Learning (FL) has become increasingly popular across different sectors, offering a way for clients to work together to train a global model without sharing sensitive data. It involves multiple rounds of communication between the global model and participating clients, which introduces several challenges like high communication costs, heterogeneous client data, prolonged processing times, and increased vulnerability to privacy threats. In recent years, the convergence of federated learning and parameterized quantum circuits has sparked significant research interest, with promising implications for fields such as healthcare and finance. By enabling decentralized training of quantum models, it allows clients or institutions to collaboratively enhance model performance and outcomes while preserving data privacy. Recognizing that Fisher information can quantify the amount of information that a quantum state carries under parameter changes, thereby providing insight into its geometric and statistical properties. We intend to leverage this property to address the aforementioned challenges. In this work, we propose a Quantum Federated Learning (QFL) algorithm that makes use of the Fisher information computed on local client models, with data distributed across heterogeneous partitions. This approach identifies the critical parameters that significantly influence the quantum model's performance, ensuring they are preserved during the aggregation process. Our research assessed the effectiveness and feasibility of QFL by comparing its performance against other variants, and exploring the benefits of incorporating Fisher information in QFL settings. Experimental results on ADNI and MNIST datasets demonstrate the effectiveness of our approach in achieving better performance and robustness against the quantum federated averaging method.