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Federated Graph Learning via Constructing and Sharing Feature Spaces for Cross-Domain IoT

IEEE Internet of Things Journal 2025年

作者： Chen, Jiale Zhuo, Shengda He, Jinchun Qiu, Wangjie Zhang, Qinnan Xiong, Zehui Zheng, Zhiming Tang, Yin Chen, Min Wang, Changdong Huang, Shuqiang Jinan University College of Cyber Security Guangzhou China Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Zhongguancun Laboratory Beijing China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore Jinan University School of Management Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Computer Guangzhou China Jinan University College of Cyber Security of Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China

The Internet of Things (IoT) collects large volumes of diverse data, with graph data as a critical component, and extensively utilizes Federated Graph Learning (FGL) to process this data while preserving data security. However, the graph data collected by different IoT institutions are relatively independent due to various factors (e.g., data collection methods, geographical locations), and data access is restricted to local environments due to privacy constraints, IoT institutions typically possess heterogeneous feature spaces. Aggregating under these conditions could potentially contaminate local graph representations. Unfortunately, most existing FGL methods tend to overlook this issue. To address this challenge, we propose Federated Graph Learning via Constructing and Sharing Features (FedCSF), a novel FGL framework designed to build a globally consistent feature space and share it among IoT institutions. To construct and extract the feature space, we employ an uniform feature initialization across IoT institutions and design an encoder to extract both global and local feature relationships, thereby facilitating effective collaboration across data from different IoT institutions. Furthermore, we introduce an independent adaptive aggregation strategy to eliminate the integration of harmful knowledge, ensuring that the contributions from each IoT institution are effectively integrated into the global model. We theoretically analyze the convergence of FedCSF. To validate the effectiveness of FedCSF, we conducted extensive experiments under various settings (i.e., cross-datasets, and cross-domains), demonstrating its significant advantages of FedCSF in terms of performance, convergence speed, and practical adaptability © 2014 IEEE.

关键词： Federated learning

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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

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IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Maximizing the pedestrian radiative cooling benefit per street tree

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Landscape and Urban Planning 2023年 230卷

作者： Lachapelle, Jacob A. Scott Krayenhoff, E. Middel, Ariane Coseo, Paul Warland, Jon School of Environmental Sciences University of Guelph Guelph ON Canada School of Arts Media and Engineering Arizona State University Tempe AZ United States School of Computing and Augmented Intelligence Arizona State University Tempe AZ United States The Design School Arizona State University Tempe AZ United States

Outdoor heat stress is a growing problem in cities during hot weather. City planners and designers require more pedestrian-centered approaches to understand sidewalk microclimates. Radiation loading, as quantified by mean radiant temperature (T_MRT), is a key factor driving poor thermal comfort. Street trees provide shade and consequently reduce pedestrian T_MRT. However, placement of trees to optimize the cooling they provide is not yet well understood. We apply the newly-developed TUF-Pedestrian model to quantify the impacts of sidewalk tree coverage on pedestrian T_MRT during summer for a lowrise neighbourhood in a midlatitude city. TUF-Pedestrian captures the detailed spatio-temporal variation of direct shading and directional longwave radiation loading on pedestrians resulting from tree shade. We conduct 190 multi-day simulations to assess a full range of sidewalk street tree coverages for five high heat exposure locations across four street orientations. We identify street directions that exhibit the largest T_MRT reductions during the hottest periods of the day as a result of tree planting. Importantly, planting a shade tree on a street where none currently exist provides approximately 1.5–2 times as much radiative cooling to pedestrians as planting the same tree on a street where most of the sidewalk already benefits from tree shade. Thus, a relatively equal distribution of trees among sun-exposed pedestrian routes and sidewalks within a block or neighbourhood avoids mutual shading and therefore optimizes outdoor radiative heat reduction per tree during warm conditions. Ultimately, street tree planting should be a place-based decision and account for additional environmental and socio-political factors. © 2022 The Author(s)

关键词： Green infrastructure design Microclimate modelling Pedestrian thermal comfort Radiative heat exposure Sidewalk shade Urban forestry

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Intelligent HMI and Control for Steering a Powered Wheelchair Using a Raspberry Pi Microcomputer 10

Intelligent HMI and Control for Steering a Powered Wheelchai...

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10th IEEE International Conference on Intelligent Systems, IS 2020

作者： Haddad, Malik Sanders, David Ikwan, Favour Thabet, Mohamad Langner, Martin Gegov, Alexander University of Portsmouth School of Mechanical and Design Engineering Portsmouth United Kingdom Chailey Heritage Foundation/Engineering Chailey United Kingdom University of Portsmouth School of Computing Portsmouth United Kingdom

ISBN: (纸本)9781728154565

This paper presents a new technique for controlling powered wheelchairs. A Raspberry Pi microcomputer is used to assist in controlling direction. A Raspberry Pi is inserted between user input switches and powered wheelchair motors to create a more intelligent Human Machine Interface (HMI). An electronic circuit is created that consists of an ultrasonic sensor array and a set of control relays. The sensors provided information about obstacles surrounding the wheelchair. Python programming language was used to create a code that digitized the output from the user switches and assessed information provided by the ultrasonic sensor array. The code was loaded onto a Raspberry Pi and the Raspberry Pi controlled voltages supplied to the motors. Tests were conducted and results showed that the new system can successfully assist a wheelchair user in avoiding obstacles. The system can be used as an intelligent interface between any input device or sensor system and wheelchair motors. © 2020 IEEE.

关键词： Python

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Sharding:A Scalability Solutions for Blockchain Networks

Sharding:A Scalability Solutions for Blockchain Networks

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Dattatray Raghunath Kale Amolkumar N Jadhav Kruishna Prasad K Somanath Janardan Salunkhe Sumit Hirve Chandrashekhar Goswami Department of Computer Science & Engg MIT School of Computing MIT Art Design and Technology University Pune India Department of Computer Science & Engg Annasaheb Dange College of Engineering and Technology Ashta India Department of Computer Science & Engg Shrinivas University Mangalore India Department of Computer Science & Engg D Y Patil Agriculture and Technical University India Faculty of Computing and Informatics Sir Padampat Singhania University Udaipur Rajasthan

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks.

关键词： Industries Sharding Scalability Memory Throughput Blockchains Security

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Identification of Diabetic Retinopathy by Transfer Learning Based Retinal Images

Identification of Diabetic Retinopathy by Transfer Learning ...

引用

International Conference on Communication and Electronics Systems (ICCES)

作者： Sreenivas Reddy Sagili Chandrashekhar Goswami V.C. Bharathi S. Ananthi K Rani R. Sathya Department of computer science Rochester Institute Of Technology Rochester New York Department of Computer Science & Engineering MIT School of Computing MIT Art Design and Technology University Pune Maharashtra School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Advanced Research Centre for Future Networks and Digital Twin Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Information Technology M. Kumarasamy College of Engineering Karur Tamilnadu India Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

The human eye is frequently considered to deliver a space into a person's strength because it is only with diabetic retinopathy (DR), a devastating eye illness caused by diabetes mellitus, that one may understand the subject's unprotected surface without invasive operations. There are certain disorders, notably vascular illness, that authority telltale indicators in the eye retina. Microaneurysms (MAs) are primary indicators of diabetic retinopathy (DR), making their detection crucial for an effective screening program that adheres to clinical procedures. Retinal examinations can reveal pathological deviations caused by limited ocular diseases such as arteriosclerosis, diabetes, cardiovascular disease hypertension and stroke. The proposed work introduces image processing algorithms, such as shady object identification, to predict the disorder or improve the contribution frame, thereby improving the perceptibility of vessels in color fundus frames. Following this enhancement, an automated classification algorithm, specifically a Convolutional Neural Network (CNN), is executed. The CNN architecture is designed to efficiently extract features from retinal frames, capturing complex patterns connected with diabetic retinopathy. The trained CNN is then assessed on a separate test condition, with its performance system of measurement-accuracy, specificity, recall, and Fl measure-being reported.

关键词： Diabetic retinopathy Pathology Image color analysis Transfer learning Retina Feature extraction Prediction algorithms Classification algorithms Convolutional neural networks Object recognition

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Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum computing

arXiv

引用

arXiv 2022年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, a novel paradigm of mobile edge-quantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings. © 2022, CC BY.

关键词： Reinforcement learning

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Satellite Federated Fine-Tuning for Foundation Models: Architecture design and System Optimization

Satellite Federated Fine-Tuning for Foundation Models: Archi...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Yan Zhu Peng Yang Jingyang Zhu Dingzhu Wen Ting Wang Yong Zhou Yuanming Shi Chunxiao Jiang MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China School of Information Science and Technology ShanghaiTech University Shanghai China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

With the surge in the number of low earth orbit (LEO) satellites, continuous research has emerged on using satellite data to train artificial intelligence models. On one hand, traditional centralized training on the ground is not feasible due to privacy concerns and limited bandwidth for downloading raw satellite data. On the other hand, due to the limited energy and computational capability of satellites, training directly on satellites suffers from prolonged latency, especially for large models. To alleviate these issues, we propose a novel satellite-ground collaborative federated fine-tuning architecture, where ground stations (GSs) and satellites collaboratively train a global model without the need for data downloads. In this proposed architecture, satellites serve as edge devices and the ground server serves as a coordinator. However, the short satellite-ground communication windows caused by the high mobility of satellites and the substantial intra-orbit data transmission bring special challenges to the transmission process of federated edge learning. To tackle these challenges, we carefully design the satellite-ground collaborative fine-tuning architecture and utilize an optimized ring all-reduce algorithm and network flow algorithm to enhance the intra-orbit and ground-satellite transmissions, respectively. Experimental results demonstrate that our proposed architecture significantly reduces the training time by 40% compared to training solely on satellite.

关键词： Training Hands Satellites Computational modeling Collaboration Low earth orbit satellites Computer architecture Data models Surges Optimization

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Inverse design of Spectral and Angular Responses of Smith-Purcell Radiation via Aperiodic Sequences

Inverse Design of Spectral and Angular Responses of Smith-Pu...

引用

Frontiers in Optics, FiO 2022

作者： Nussupbekov, Ayan Png, Ching Eng Wu, Lin Wong, Liang Jie Chong, Yi Dong Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore Science Mathematics and Technology Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore637371 Singapore

We demonstrate, using time domain finite difference simulations, that Smith-Purcell radiation can be precisely tailored. Spectral and spatial emission shaping is achieved by optimizing a grating parameterized by aperi...

ISBN: (纸本)9781557528209

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A System to Track the Behaviour or Pattern of Mobile Robot Through RNN Technique

A System to Track the Behaviour or Pattern of Mobile Robot T...

引用

Advance computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： E.Afreen Banu Senthilnathan Chidambaranathan Naduvathezhath Nessariose Jose Padmaja Kadiri Riyad E. Abed Aqeel Al-Hilali Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Architecture and Design Virtusa New Jersey USA Regent Strategic Solutions Rochester United States Department of AIML School of Computing Mohan Babu University Tirupati Andhra Pradesh India College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The agricultural business has seen a rise in the use of mobile robots because of their ease of navigation in crop fields. Numerous significant issues pertaining to agricultural robot navigation are covered in this paper, such as obstacle detection, route planning, mapping, localization, and controlled guiding. The proposed hybrid recurrent neural network (RNN) model seeks to optimise mobile autonomous robot tracking performance using installed laser-based weeding equipment. The system can handle a range of trajectories, including curves and straight lines, that are observed in agricultural settings by integrating spiral, lateral, and linear speed controls. A comprehensive field test is carried out on a tracked mobile platform to validate the controllers’ functionality in a variety of scenarios, including loose dirt, stones, and humidity.

关键词： Location awareness Recurrent neural networks Spirals Navigation Velocity control Crops Humidity

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