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BPFL: Blockchain-Enabled Distributed Edge Cluster for Personalized Federated Learning 14th

BPFL: Blockchain-Enabled Distributed Edge Cluster for Person...

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14th international conference on Computer Science and its Applications, CSA 2022 and the 16th KIPS international conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： Firdaus, Muhammad Noh, Siwan Qian, Zhuohao Rhee, Kyung-Hyune Department of Artificial Intelligence Convergence Pukyong National University Busan48513 Korea Republic of Department of Information Security Pukyong National University Busan48513 Korea Republic of Division of Computer Engineering Pukyong National University Busan48513 Korea Republic of

ISBN: (纸本)9789819912513

Due to the fact that the client's data distribution in federated learning (FL) is highly heterogeneous and leads to poor convergence, the concept of personalized federated learning (PFL) is on the rise. PFL aims to tackle the effect of non-IID data and statistical heterogeneity problems along with achieving rapid model convergence and personalized models. Moreover, in the context of clustering-based PFL, it allows a multi-model method with group-level client relationships to accomplish personalization. However, since the current method still relies on the centralized method, where the server orchestrates all processes, this paper introduces a blockchain-enabled distributed edge cluster for PFL (BPFL) that exploits the benefits of blockchain and edge computing to address these shortcomings. Blockchain can be employed to enhance client privacy and security by recording all transactions in immutable distributed ledger networks as well as improving efficient client selection and clustering. Furthermore, an edge computing system offers reliable storage and computation, where computational processing is locally performed in the edge infrastructure to be closer to clients. Thus, it also improves real-time services and low-latency communication of PFL. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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State Estimation of Electromagnetic Scanning Micro-Mirrors Using Unscented Kalman Filtering Method*

State Estimation of Electromagnetic Scanning Micro-Mirrors U...

引用

2023 IEEE international conference on Real-Time computing and Robotics, RCAR 2023

作者： Yang, Sen Tan, Yonghong Tan, Qingyuan Dong, Ruili Shanghai Normal University College of Mathematics and Physics Shanghai200234 China Shanghai Normal University College of Information Mechnical and Electrical Engineering Shanghai200234 China University of Windsor Windsor Canada Donghua University College of Information Science and Technology Shanghai201620 China

ISBN: (纸本)9798350327182

A nonsmooth dynamic filtering strategy with the frame of unscented Kalman filter (UKF) is proposed for state estimation of electromagnetic scanning micromirrors (ESM) contaminated by random noise. In this method, a stochastic nonsmooth sandwich state-space model with rate-dependent hysteresis is developed to depict the characteristics of the ESM disturbed by random noise. Then, the UKF scheme for the state estimation of ESM system is developed. Afterward, the convergence is provided. Subsequently, a simulated example is illustrated to evaluate the behavior of the proposed filtering method. Finally, experimental results to the state estimation of an ESM are presented. © 2023 IEEE.

关键词： State estimation

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An Evolutionary Reinforcement Learning Scheme for IoT Robustness 26

An Evolutionary Reinforcement Learning Scheme for IoT Robust...

引用

26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yang, Xinwei Chen, Ning Zhang, Songwei Zhou, Xiaobo Zhang, Lejun Qiu, Tie Tianjin University College of Intelligence and Computing Tianjin300350 China Guangzhou University Cyberspace Institute Advanced Technology Guangzhou510006 China

ISBN: (纸本)9798350331684

With the rapid scale expansion of the Internet of Things (IoT), the probability of system failure increases. Frequent system failures degrade the quality of service (QoS) of IoT. Existing optimization strategies utilize reinforcement learning (RL) to enhance the robustness of IoT topology. However, due to the increasing scale of the IoT environment, the unbalanced exploration and exploitation of RL agents make it prone to premature convergence at the local optimum. Large-scale action spaces and state spaces lead to a sparse reward problem, which reduces the convergence efficiency of the algorithm. This paper proposes an evolutionary reinforcement learning scheme for IoT robustness to solve the above problems. We design a multi- agent evolution mechanism to provide multiple experiences for RL, which strengthens exploration capability. We present new evolution operators to promote convergence, which combine dis- tillation crossover and Gaussian mutation. Extensive experiments show that our scheme has a strong exploration capability, and the optimization rate of IoT topology robustness reaches 81.15%, which outperforms other robustness optimization algorithms. © 2023 IEEE.

关键词： Network topology

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A Vector Routing Protocol for computing and Network convergence in Digital Grid Scenario 7

A Vector Routing Protocol for Computing and Network Converge...

引用

7th international conference on Computer, Software and Modeling, ICCSM 2023

作者： Lu, Jiangang Zhang, Peiming Liu, Yuanjie Power Dispatching Control Center Guangdong Power Grid Co. Ltd Guangzhou China

ISBN: (纸本)9798350305814

In an industrial Internet with clear hierarchical division like China’s digital grid, real-time and fast intelligent decision-making for power production control at the local field level can be supported by cloud-edge computing. In the cloud-edge fusion within this scenario, there will be a large amount of information interaction and data transmission between entities in different locations, which also involves differentiated routing decisions for computing tasks. Therefore, it is of great significance to comprehensively consider the computing resources and network resources in the digital grid to schedule the computing tasks in the above scenario. Distributed routing is a routing method relative to centralized control routing (such as SDN, software-defined network). Each individual distributed router calculates the routing table itself and then determines the forwarding strategy based on the previous calculation results. Such as EIGRP (Enhanced Interior Gateway Routing Protocol), DSDV (Destination Distance Vector Protocol), etc. Based on the distributed vector routing technology, this paper proposes a distributed routing protocol that can comprehensively consider the existing computing resources and network resources (delay, bandwidth, etc.) in the network for calculation, and designs a simulation test system. The system can virtualize a small-scale network including several routing nodes, and complete processes such as neighbor discovery, route notification, route computation, and data forwarding, thereby verifying the availability of routing protocols. © 2023 IEEE.

关键词： Decision making

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Innovations and Challenges in Green Cloud computing via LEED, Ant Colony Optimization 3

Innovations and Challenges in Green Cloud Computing via LEED...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Rohit, V. Robin Saravanan, S.K. Lenin, J. Nisha, F. Sivakumar, S. Rajmohan, M. Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Karnataka Bengaluru India Saveetha School of Engineering Simats Department of BigData and Network Security Tamil Nadu Chennai India Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Karnataka Bengaluru India School of Engineering and Technology Computer Science & Engineering Cmr University Karnataka Bengaluru India Department of Computer Science Tamil Nadu Perambalur India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350319125

This paper investigates how Green Cloud computing may be made more efficient by combining LEED standards with Ant Colony Optimization (ACO), which is an approach to cloud computing that takes environmental considerations into account. The goal is to maximize resource allocation in data centers while minimizing energy use and carbon impact. The innovative combination of ACO and LEED offers a fresh perspective on achieving a balance between computational performance and environmental sustainability. The goal is to design a system that uses ACO-driven algorithms to optimize energy consumption in cloud infrastructure so it meets LEED standards. Innovative solutions for large-scale cloud settings are needed to address the challenges of scalability and convergence speed that ACO faces. Further, in order to widen the scope, Multi-Objective Optimization (MOO) and Particle Swarm Optimization (PSO) are investigated. While MOO has it tough dealing with competing goals, PSO has trouble striking a balance between the two. We can learn more about how to optimize cloud operations while reducing environmental effect thanks to innovations in these fields. By solving problems in ACO, PSO, and MOO, this study paves the way for Green Cloud computing to become more efficient and less harmful to the environment. © 2023 IEEE.

关键词： Ant Colony Optimization Challenges Energy Environmental Design Green Cloud computing Innovations Leadership Resource Allocation Sustain ability technology

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Blockchain-based Decentralized Trust Aggregation for Federated Cyber-Attacks Classification in SDN-Enabled Maritime Transportation Systems

Blockchain-based Decentralized Trust Aggregation for Federat...

引用

IEEE international conference on Communications (IEEE ICC)

作者： Zainudin, Ahmad Alief, Revin Naufal Putra, Made Adi Paramartha Akter, Rubina Kim, Dong-Seong Lee, Jae-Min Kumoh Natl Inst Technol Dept Elect Engn Gumi 39177 South Korea Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi 39177 South Korea Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi 39177 South Korea Politekn Elekt Negeri Surabaya Dept Elect Engn Surabaya Indonesia

ISBN: (纸本)9798350333077

Maritime transportation systems (MTS) based on software-defined networks (SDN) offer reliability, efficiency, and flexibility in network connection and configuration. However, these abilities make an SDN-based MTS with a single controller vulnerable to cyber-attacks. A centralized-based intrusion detection system (IDS) technique requires uploading the network traffic features as well as training data to the cloud server. An SDN-based MTS that is concerned with data privacy makes it challenging. This study proposes a blockchain-based federated cyber-attacks classification framework to maintain data in a decentralized manner, raise privacy concerns, and perform a secure decentralized aggregation mechanism. A proof-of-authority (PoA)-based blockchain network with an interplanetary file system (IPFS) as off-chain distributed storage is implemented to provide a secure and trusted federated IDS framework. Moreover, the proposed lightweight CNN-based IDS model used a residual connection and factorized convolution structure to fix the gradient vanishing issue and provide a low-complexity network structure. The model measurement shows that the proposed model performs better than state-of-the-art models in terms of accuracy, computing cost, and model design complexity.

关键词： blockchain federated learning decentralized aggregation intrusion detection software-defined networks (SDN) maritime transportation systems (MTS)

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AFL-CS: Asynchronous Federated Learning with Cosine Similarity-based Penalty Term and Aggregation 29

AFL-CS: Asynchronous Federated Learning with Cosine Similari...

引用

29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yan, Bingjie Jiang, Xinlong Chen, Yiqiang Gao, Chenlong Liu, Xuequn Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China University of Chinese Academy of Sciences Beijing China Nanchang Aier Eye Hospital Nanchang China

ISBN: (纸本)9798350330717

Horizontal Federated Learning offers a means to develop machine learning models in the realm of medical application while preserving the confidentiality and security of patient data. However, due to the substantial heterogeneity of the devices in medical institution, traditional synchronous federated aggregation methods result in a noticeable decrease in training efficiency, thereby impacting the application and deployment of federated learning. Asynchronous Federated Learning (AFL) model aggregation methods can mitigate this problem but present new challenges in terms of convergence stability and speed. In this paper, we propose a cosine similarity-based layer-wise penalty term and asynchronous model aggregation method AFL-CS, which considers the global model convergence direction during local training. Compared with existing AFL aggregation methods, AFL-CS can achieve faster and more consistent convergence direction to superior performance especially in non-iid settings with high statistical heterogeneity, even reaching and exceeding synchronous FL. © 2023 IEEE.

关键词： asynchronous federated learning medical application model aggregation privacy protection

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A Lightweight Software Stack for IoT Interoperability within the computing Continuum 19

A Lightweight Software Stack for IoT Interoperability within...

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19th Annual international conference on Distributed computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Spatharakis, Dimitrios Dimolitsas, Ioannis Genovese, Giacomo Tzanettis, Ioannis Filinis, Nikos Fotopoulou, Eleni Vassilakis, Constantinos Zafeiropoulos, Anastasios Iera, Antonio Molinaro, Antonella Papavassiliou, Symeon School of Electrical and Computer Engineering National Technical University of Athens Greece University Mediterranea of Reggio CINT Dept. DIIES Calabria Italy University of Calabria CINT Dept. DIMES Italy

ISBN: (纸本)9798350346497

With the rapid advancement in the next-generation Internet of Things (IoT) and the ever-expanding virtual representation of devices, novel techniques and definitions are key to accommodate emerging paradigms to transform our world. In the quest of achieving the coveted convergence of IoT technologies with the Cloud-Edge computing continuum and the seamless interaction of IoT devices and hyper-distributed applications, we enhance the concept of Virtual Objects (VOs) for the virtualization of IoT devices in the wake of recent works. We introduce a software stack, called VOStack, to enable interoperability and convergence between the physical IoT devices layer and the Edge/Cloud virtualized infrastructure. The VOStack facilitates interoperability with IoT devices and provides a unified way for their control, enables smooth lifecycle management of the VOs, and allows the development of modern distributed applications by offering on-demand capabilities to support IoT application operations. The necessary interactions of the VOs are presented along with the essential core functionalities for the development of the VOStack. Finally, an overview of different deployment models aligned with the state of practice of infrastructure providers is detailed. © 2023 IEEE.

关键词： Interoperability

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Hybrid Population Intelligence and Deep Learning Research for Software Defect Prediction 3

Hybrid Population Intelligence and Deep Learning Research fo...

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3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Pei, Guohong Lu, Zhijiong Jin, Jiaxing Feng, Rongqing Zhuhai College of Science and Technology Guangdong China

ISBN: (纸本)9798350343922

The goal of this project is to explore the topic of software defect prediction and provide a novel method for enhancing prediction model performance by fusing hybrid crowd intelligence with deep learning. In the study, we first use deep learning autoencoder for data dimensionality reduction, and achieve supervised pre-training of high-dimensional data by learning the intrinsic features of the data. Then, a deep neural network-based software defect prediction model was constructed, in which a hybrid population intelligence algorithm was used for hyperparameter optimisation. The hybrid algorithm combines the advantages of particle swarm optimisation and wolf pack algorithms for better global exploration and local optimisation when searching the hyperparameter space. In our experiments, we used a software defect dataset containing 58 features and compared the performance of five hyperparameter optimisation methods such as grid algorithm, stochastic algorithm, particle swarm algorithm, wolf pack algorithm and hybrid wolf pack algorithm. The experimental results show that the model using the hybrid wolfpack algorithm exhibits significant advantages in four evaluation metrics, including precision, recall, F-measure and AUC, and achieves more robust software defect prediction compared to other methods. This study not only promotes the application of deep learning in the field of software engineering, but also provides new empirical support for the effectiveness of group intelligence algorithms in software defect prediction. © 2023 IEEE.

关键词： Prediction models

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Blockchain and Reverse Auction-based EVs Energy Trading Approach for Optimal Pricing 13

Blockchain and Reverse Auction-based EVs Energy Trading Appr...

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13th international conference on Cloud computing, Data Science and Engineering, Confluence 2023

作者： Kakkar, Riya Gupta, Rajesh Jadav, Nilesh Kumar Tanwar, Sudeep Agrawal, Smita Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India

ISBN: (纸本)9781665462631

This paper proposes a reverse auction and blockchain-based electric vehicles (EVs) energy trading (ET) approach by adopting the smart grid. EVs can be classified into prosumer or consumer during the ET in the proposed approach. We further introduced the Interplanetary File System (IPFS)-based cost-efficient ET approach considering the sixth-generation (6G) wireless network to address the scalability and response time issues of the data transactions. Furthermore, the proposed approach leverages a reverse auction mechanism for optimal ET for EVs. The reverse auction mechanism maximizes the profit for EVs participating in the ET scheme using smart grid. Based on the reverse auction mechanism, a consumer can choose to trade energy with the prosumer or smart grid based on solar panel efficiency. Finally, the proposed approach is simulated considering the various performance metrics such as transaction efficiency, profit for consumers, and convergence to enable an optimal and efficient ET system. © 2023 IEEE.

关键词： Blockchain

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