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Privacy-preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet

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arXiv 2022年

作者： Xu, Minrui Niyato, Dusit Yang, Zhaohui Xiong, Zehui Kang, Jiawen Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China Zhejiang Laboratory Hangzhou31121 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore 487372 Singapore The School of Automation Guangdong University of Technology China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL is still facing model confidentiality issues due to eavesdropping risks of exchanging cryptographic keys through traditional encryption schemes. Therefore, in this paper, we propose a hierarchical architecture for quantum-secured FEL systems with ideal security based on the quantum key distribution (QKD) to facilitate public key and model encryption against eavesdropping attacks. Specifically, we propose a stochastic resource allocation model for efficient QKD to encrypt FEL keys and models. In FEL systems, remote FEL workers are connected to cluster heads via quantum-secured channels to train an aggregated global model collaboratively. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is unpredictable. The proposed systems need to efficiently allocate limited QKD resources (i.e., wavelengths) such that the total cost is minimized in the presence of stochastic demand by formulating the optimization problem for the proposed architecture as a stochastic programming model. To this end, we propose a federated reinforcement learning-based resource allocation scheme to solve the proposed model without complete state information. The proposed scheme enables QKD managers and controllers to train a global QKD resource allocation policy while keeping their private experiences local. Numerical results demonstrate that the proposed schemes can successfully achieve the cost-minimizing objective under uncertain demand while improving the training efficiency by about 50% compared to state-of-the-art schemes. © 2022, CC BY.

关键词： Stochastic systems

Over-the-Air Federated Averaging with Limited Power and Privacy Budgets

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arXiv 2023年

作者： Yan, Na Wang, Kezhi Pan, Cunhua Keong, Kok Shu, Feng Wang, Jiangzhou School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Computer Science Brunel University London Middlesex UxbridgeUB8 3PH United Kingdom The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of Information and Communication Engineering Hainan University Haikou570228 China The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China The School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

To jointly overcome the communication bottleneck and privacy leakage of wireless federated learning (FL), this paper studies a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system with a limited sum power budget. With DP-OTA-FedAvg, the gradients are aligned by an alignment coefficient and aggregated over the air, and channel noise is employed to protect privacy. We aim to improve the learning performance by jointly designing the device scheduling, alignment coefficient, and the number of aggregation rounds of federated averaging (FedAvg) subject to sum power and privacy constraints. We first present the privacy analysis based on differential privacy (DP) to quantify the impact of the alignment coefficient on privacy preservation in each communication round. Furthermore, to study how the device scheduling, alignment coefficient, and the number of the global aggregation affect the learning process, we conduct the convergence analysis of DP-OTA-FedAvg in the cases of convex and non-convex loss functions. Based on these analytical results, we formulate an optimization problem to minimize the optimality gap of the DP-OTA-FedAvg subject to limited sum power and privacy budgets. The problem is solved by decoupling it into two sub-problems. Given the number of communication rounds, we conclude the relationship between the number of scheduled devices and the alignment coefficient, which offers a set of potential optimal solution pairs of device scheduling and the alignment coefficient. Thanks to the reduced search space, the optimal solution can be efficiently obtained. The effectiveness of the proposed policy is validated through simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Budget control

A Tutorial on Movable Antennas for Wireless Networks

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arXiv 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Chengdu611731 China National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electronic and Information Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada State Key Laboratory of CNS/ATM MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

A Review on Quantum Approximate Optimization Algorithm and its Variants

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arXiv 2023年

作者： Blekos, Kostas Brand, Dean Ceschini, Andrea Chou, Chiao-Hui Li, Rui-Hao Pandya, Komal Summer, Alessandro Department of Physics School of Natural Sciences University of Patras Patras26504 Greece Department of Physics Stellenbosch University Stellenbosch7600 South Africa Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome00184 Italy Department of Engineering Science and Ocean Engineering National Taiwan University Taipei10617 Taiwan Department of Physics Case Western Reserve University ClevelandOH44106 United States Department of Computer Science and Engineering Indian Institute of Technology Patna Patna801106 India School of Physics Trinity College Dublin Dublin 2 Ireland

The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization problems that are classically intractable. This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios, its applicability across various problem instances, and considerations of hardware-specific challenges such as error susceptibility and noise resilience. Additionally, we conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm. We aim to provide insights into key questions about the algorithm, such as whether it can outperform classical algorithms and under what circumstances it should be used. Towards this goal, we offer specific practical points in a form of a short guide. © 2023, CC BY.

关键词： Combinatorial optimization

Advancing quantum imaging through learning theory

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arXiv 2025年

作者： Wang, Yunkai Oh, Changhun Liu, Junyu Jiang, Liang Zhou, Sisi Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Department of Applied Mathematics University of Waterloo ONN2L 3G1 Canada Institute for Quantum Computing University of Waterloo ONN2L 3G1 Canada Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Computer Science The University of Pittsburgh PittsburghPA15260 United States Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States Department of Physics and Astronomy University of Waterloo ONN2L 3G1 Canada

We quantify performance of quantum imaging by modeling it as a learning task and calculating the Resolvable Expressive Capacity (REC). Compared to the traditionally applied Fisher information matrix approach, REC provides a single-parameter interpretation of overall imaging quality for specific measurements that applies in the regime of finite samples. We first examine imaging performance for two-point sources and generally distributed sources, referred to as compact sources, both of which have intensity distributions confined within the Rayleigh limit of the imaging system. Our findings indicate that REC increases stepwise as the sample number reaches certain thresholds, which are dependent on the source’s size. Notably, these thresholds differ between direct imaging and superresolution measurements (e.g., spatial-mode demultiplexing (SPADE) measurement in the case of Gaussian point spread functions (PSF)). REC also enables the extension of our analysis to more general scenarios involving multiple compact sources, beyond the previously studied scenarios. For closely spaced compact sources with Gaussian PSFs, our newly introduced orthogonalized SPADE method outperforms the naively separate SPADE method, as quantified by REC. Copyright © 2025, The Authors. All rights reserved.

关键词： Fisher information matrix

Higher education assessment practice in the era of generative AI tools

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arXiv 2024年

作者： Ogunleye, Bayode Zakariyyah, Kudirat Ibilola Ajao, Oluwaseun Sharma, Hemlata Olayinka, Olakunle School of Architecture Technology & Engineering University of Brighton BrightonBN2 4GJ United Kingdom Department of Computing & Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Department of Computer Science University of Sheffield SheffieldS1 4DP United Kingdom Department of Computing Sheffield Hallam University SheffieldS1 2NU United Kingdom

The higher education (HE) sector benefits every nation’s economy and society at large. However, their contributions are challenged by advanced technologies like generative artificial intelligence (GenAI) tools. In this paper, we provide a comprehensive assessment of GenAI tools towards assessment and pedagogic practice and, subsequently, discuss the potential impacts. This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines. Our findings are two-fold: first, the findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills and thus can limit learning when used unethically. Secondly, the design of the assessment of certain disciplines revealed the limitations of the GenAI tools. Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE. © 2024, CC BY.

关键词： Project management

Dual-Band Dual-Polarized Array Based on Electromagnetic Transparent Antenna for Vehicle-Mounted Base Station Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Xuekang Sanz-Izquierdo, Benito Gao, Steven Luo, Qi Zhang, Lu Wen, Lehu Yang, Xue-Xia Wang, Lei Lancaster University School of Engineering LancasterLA1 4YW United Kingdom University of Kent School of Engineering CanterburyCT2 7NT United Kingdom Chinese University of Hong Kong Dept of Electronic Engineering Hong Kong University of Hertfordshire School of Physics Engineering and Computer Science HatfieldAL10 9AB United Kingdom Swansea University Faculty of Science and Engineering SwanseaSA1 8EN United Kingdom Brunel University London Department of Electronic and Electrical Engineering UxbridgeUB8 3PH United Kingdom Shanghai University School of Communication and Information Engineering Shanghai200444 China

This paper introduces a novel ±45° shared-aperture, dual-band, dual-polarized interleaved array for vehicle-mounted base station systems. The array integrates a lower band (LB) antenna with four higher band (HB) antennas. To address the issue of cross-band scattering and its impact on higher band performance, we employ an innovative method that involves reducing the aperture size and incorporating lumped or distributed inductors. These inductors function as frequency-selective structures, permitting low-frequency currents to pass while blocking high-frequency currents. Consequently, at high frequencies, these inductors act as open circuits, making the low-frequency antenna radiator resemble multiple discontinuous small metal strips. This approach achieves an electromagnetic transparent (EMT) bandwidth of over 82.9%, making it highly suitable for mobile base station applications. By integrating this low-scattering LB antenna with four bandwidth-optimized cross dipoles, we achieve a dual-band interleaved array. The measured results confirm that the dual-band array operates effectively within the 0.85-0.98 GHz and 1.4-2.7 GHz frequency ranges, maintaining stable radiation performance. The superior performance of this design makes it an ideal choice for vehicle-based base stations. © 1967-2012 IEEE.

关键词： Strip metal

Semantic Substructure Guided Multiple Objective Molecular Generation with Discrete Diffusion Probabilistic Model

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SSRN

SSRN 2023年

作者： Song, Bosheng Chen, Shugao Zhao, Chen Wang, Zixu Chen, Ruizhe Liu, Yuansheng Li, Chunyan Zeng, Xiangxiang College of Information Science and Engineering Hunan University 2 Lushan S Rd Yuelu District Hunan Changsha410086 China School of Informatics Yunnan Normal University No.1 Yuhua Chenggong District Yunnan Kunming650500 China Department of Computer Science University of Tsukuba Tenoudai 1-1-1 Tsukuba Science City Ibaraki3058573 Japan

Deep learning methods have paved the highway for drug discovery in past few years, it is still a big challenge that designed molecules satisfy multiple objective constraints. Traditional generative adversarial networks suffer sever mode collapse problem and Transformer based language models require relative dataset to fine-tune. Denoising diffusion probabilistic models (DDPMs) has achieved huge success in image generation fields, but have limiting explored in drug discovery. We bridges the molecule prior distribution with the desired bioactive molecular distribution and propose SGDiff, Substructure Guided Diffusion model, which can iteratively inject the conditional information into the denoising process to guide the generation process without additional reinforcement learning fine-tuning process. SGDiff can achieve the highest conditional success rate in multiple objective generation tasks and can maintain high diversity from conditional guidance. Besides, SGDiff has a great interpretation during sampling. A lot of explorations have been conducted to detail understanding the mechanism of the guidance paradigm, which can be better used for further multiple objective constraints molecular generation. © 2023, The Authors. All rights reserved.

关键词： Molecules

Scalable Stability of Nonlinear Interconnected Systems in case of amplifying Perturbations

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Scalable Stability of Nonlinear Interconnected Systems in ca...

American Control Conference (ACC)

作者： Marco Mirabilio Alessio Iovine Department of Information Engineering Computer Science and Mathematics Center of Excellence DEWS University of L’Aquila Laboratory of Signal and Systems (L2S) Centre National de la Recherche Scientifique (CNRS) CentraleSupélec Paris-Saclay University Gif-sur-Yvette France

This paper investigates the stability of large-scale systems (LSSs) in the presence of subsystems that amplify the perturbations propagated by their neighborhood, possibly leading to undesired behaviors of the overall interconnected system. Then, sufficient conditions ensuring the system trajectories boundedness and the subsequent LSS asymptotic stability in the sense of scalable Mesh Stability are proven to exist. The theoretical results show that there exists a dependence between the stability and the topology of the interconnected system. The obtained framework is then exploited for the stability analysis of a network of electrical microgrids, showing the effectiveness of the theoretical results.

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