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Energy Efficient Collaborative Federated Learning Design: A Graph Neural Network based Approach

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Energy Efficient Collaborative Federated Learning Design: A ...

IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

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Energy grid management system with anomaly detection and Q-learning decision modules

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computers and electrical engineering 2023年 107卷

作者： Syu, Jia-Hao Srivastava, Gautam Fojcik, Marcin Cupek, Rafal Lin, Jerry Chun-Wei Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Mathematics and Computer Science Brandon University Brandon Canada Research Center for Interneural Computing China Medical University Taichung Taiwan Department of Computer Science and Math Lebanese American University Beirut Lebanon Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

Stability and security issues in energy management have become widespread research topics, in which artificial intelligence techniques are often embedded in management systems to efficiently manage the smart grid. In this paper, we propose an energy grid management system with anomaly detection and Q-learning decision modules (EMSAD). The anomaly detection module is a multitask learning network that simultaneously classifies suppliers and predicts actual supply quantities. The Q-learning decision module then determines the operating reserve and subsidies to manage the energy grid. Experimental results show that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers with F1-scores from 73.3% to 100.0%. The robustness evaluation also shows that EMSAD maintains high performance even in unseen environments without fine-tuning. Thus, the simulation results demonstrate the security, efficiency, transferability, and robustness of the proposed EMSAD in smart grid energy management. © 2023 Elsevier Ltd

关键词： Energy management

A roadmap for the commercialization of perovskite light emitters (August, 10.1038/s41578-022-00459-4, 2022)

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NATURE REVIEWS MATERIALS 2022年第10期7卷 833-833页

作者： Han, Tae-Hee Jang, Kyung Yeon Dong, Yitong Friend, Richard H. Sargent, Edward H. Lee, Tae-Woo Hanyang University Division of Materials Science and Engineering Seoul Republic of Korea (GRID:grid.49606.3d) (ISNI:0000 0001 1364 9317) Seoul National University Department of Materials Science and Engineering Seoul Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905) University of Toronto Department of Electrical and Computer Engineering Toronto Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938) University of Cambridge Cavendish Laboratory Cambridge UK (GRID:grid.5335.0) (ISNI:***) Seoul National University Department of Materials Science and Engineering Seoul Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905) Seoul National University School of Chemical and Biological Engineering Institute of Engineering Research Research Institute of Advanced Materials Soft Foundry Seoul Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)

Metal halide perovskites (MHPs) possess advantageous optoelectronic properties, so perovskite emitters and perovskite light-emitting diodes (PeLEDs) are promising candidates for next-generation high-colour-purity displays and lighting applications. Within the past 5 years, the luminescence efficiency of MHP emitters and PeLEDs has increased rapidly. However, the industrial applications of perovskites are impeded by several technical bottlenecks, such as insufficient colour reproducibility, low operational stability, toxicity and limited large-scale production. In this Review, we survey the current status of MHP emitters and PeLEDs and provide a technical roadmap to highlight the goals and requirements for them to successfully enter the markets for vivid displays with high colour purity, augmented and virtual reality displays and general and special lighting. We also set out steps for future research in MHPs and their device *** perovskite light emitters hold promise for next-generation high-colour-purity displays and lighting applications. This Review surveys the outstanding scientific issues and the strategies to increase the efficiency and stability of perovskite light-emitting devices, tracing a roadmap for their mass production and commercialization.

关键词： Perovskite

LANCAR: Leveraging Language for Context-Aware Robot Locomotion in Unstructured Environments

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arXiv 2023年

作者： Shek, Chak Lam Wu, Xiyang Suttle, Wesley A. Busart, Carl Zaroukian, Erin Manocha, Dinesh Tokekar, Pratap Bedi, Amrit Singh Department of Electrical and Computer Engineering University of Maryland College ParkMD United States DEVCOM Army Research Laboratory AdelphiMD United States Department of Computer Science University of Maryland College ParkMD United States Department of Computer Science University of Central Florida OrlandoFL United States

Navigating robots through unstructured terrains is challenging, primarily due to the dynamic environmental changes. While humans adeptly navigate such terrains by using context from their observations, creating a similar context-aware navigation system for robots is difficult. The essence of the issue lies in the acquisition and interpretation of context information, a task complicated by the inherent ambiguity of human language. In this work, we introduce LANCAR, which addresses this issue by combining a context translator with reinforcement learning (RL) agents for context-aware locomotion. LANCAR allows robots to comprehend context information through Large Language Models (LLMs) sourced from human observers and convert this information into actionable context embeddings. These embeddings, combined with the robot’s sensor data, provide a complete input for the RL agent’s policy network. We provide an extensive evaluation of LANCAR under different levels of context ambiguity and compare with alternative methods. The experimental results showcase the superior generalizability and adaptability across different terrains. Notably, LANCAR shows at least a 7.4% increase in episodic reward over the best alternatives, highlighting its potential to enhance robotic navigation in unstructured environments. More details and experiment videos could be found in this link. © 2023, CC BY.

关键词： Reinforcement learning

electrical Tuning of Neutral and Charged Excitons with 1-nm Gate

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arXiv 2023年

作者： Almutlaq, Jawaher Wang, Jiangtao Li, Linsen Li, Chao Dang, Tong Bulović, Vladimir Kong, Jing Englund, Dirk Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

electrical control of individual spins and photons in solids is key for quantum technologies, but scaling down to small, static systems remains challenging. Here, we demonstrate nanoscale electrical tuning of neutral and charged excitons in monolayer WSe2 using 1-nm carbon nanotube gates. Electrostatic simulations reveal a confinement radius below 15 nm, reaching the exciton Bohr radius limit for few-layer dielectric spacing. In situ photoluminescence spectroscopy shows gate-controlled conversion between neutral excitons, negatively charged trions, and biexcitons at 4 K. Important for quantum information processing applications, our measurements indicate gating of a local 2D electron gas in the WSe2 layer, coupled to photons via trion transitions with binding energies exceeding 20 meV. The ability to deterministically tune and address quantum emitters using nanoscale gates provides a pathway towards large-scale quantum optoelectronic circuits and spin-photon interfaces for quantum networking. © 2023, CC BY.

关键词： Selenium compounds

AC/DC: Automated Compilation for Dynamic Circuits

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arXiv 2024年

作者： Niu, Siyuan Kökcü, Efekan Mitra, Anupam Szasz, Aaron Hashim, Akel Kalloor, Justin de Jong, Wibe Albert Iancu, Costin Younis, Ed Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Quantum Nanoelectronics Laboratory Department of Physics University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science University of California BerkeleyCA94720 United States

Dynamic quantum circuits incorporate mid-circuit measurements and feed-forward operations originally intended to realize Quantum Error Correction. This paradigm has recently been utilized to prepare certain states and long-range entangling gates as well as reduce resource overhead in quantum algorithms such as Quantum Fourier Transformation and Quantum Phase Estimation. In this paper, we present a novel framework for generating dynamic quantum circuits that automatically prepare any state or unitary operator. This procedure is powered by numerical optimization-based circuit synthesis methods. The first contribution is introducing optimization objective functions incorporating mid-circuit measurement and feed-forward operations. The second contribution is incorporating these into a popular open-source quantum circuit synthesis framework. We demonstrate the generation of dynamic circuits for state preparation, long-range entangling gates, circuit optimization, and the application of dynamic circuits to lattice simulations. The resulting circuits are validated through simulation as well as through execution on quantum hardware. Furthermore, we perform noise analysis to explore the impact of different error ratios in mid-circuit measurements and gate errors, identifying scenarios where dynamic circuits offer the most significant benefits. The dynamic circuits generated by our framework show substantial improvements in reducing circuit depth and, in some cases, the number of gates required. To our knowledge, this is the first practical procedure to generate dynamic quantum circuits. Our objective functions are independent of the underlying synthesis framework and can be easily reused. The framework opens new possibilities for circuit generation and optimization methods, and the current results highlight the potential of dynamic circuits to enhance the performance of quantum algorithms on near-term quantum computers. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

Cross-View Fuzziness and Intra-view Uncertainty-Based Weight Reconstruction for Multi-view Feature Selection 11th

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Cross-View Fuzziness and Intra-view Uncertainty-Based Weigh...

11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Xiang, Biao Chen, Hongmei Mi, Yong Sang, Binbin Horng, Shi-Jinn Li, Tianrui Institute of Artificial Intelligence School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Southwest Jiaotong University Chengdu 611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 China College of Computer and Information Science Chongqing Normal University Chongqing 401331 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan

ISBN: (纸本)9783031927430

Weight assignment is a universal and indispensable process in multi-view feature selection. However, most existing methods overlook the fuzziness and uncertainty implied in multi-view data. This may result in improper weight assignment, degrading the quality of selected features. A cross-view fuzziness and intra-view uncertainty-based weight reconstruction method (CFIUWR) is proposed for multi-view feature selection. With the fuzzy rough set theory, fuzzy approximation operators recognize cross-view fuzziness and intra-view uncertainty, which are evaluated by classification-related measurement. Cross-view fuzziness and intra-view uncertainty are incorporated to develop a reconstruction mechanism for view and feature weights. An optimization algorithm is derived, guaranteeing rapid convergence. Comparisons with state-of-the-art methods on several real-world datasets demonstrate the superiority of the CFIUWR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature selection Fuzzy rough set Multi-view Weight reconstruction

Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming

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arXiv 2024年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Hong Kong University 999077 Hong Kong

Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of UAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications. However, due to the open nature of UAV channels, security issue requires special consideration. This paper proposes a dual UAV cluster-assisted system via CB to achieve physical layer security in maritime wireless communications. Specifically, one UAV cluster forms a maritime UAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other UAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of UAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

A Tutorial on Movable Antennas for Wireless Networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore National Key Laboratory of Wireless Communications Chengdu611731 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Beihang University School of Electronic and Information Engineering Beijing100191 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Institute of Technology State Key Laboratory of CNS/ATM Miit Key Laboratory of Complex-field Intelligent Sensing Beijing100081 China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data 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