Article
Computer Science, Information Systems
Batyr Charyyev, Mehmet Hadi Gunes
Summary: Engineered systems are becoming smarter thanks to computing capabilities and the increasing number of IoT devices. However, IoT devices are vulnerable to compromise due to their limited resources, making them prime targets for malicious activities. This article introduces a novel approach using locality-sensitive hash to identify IoT devices based on their traffic flow, achieving high precision and recall without the need for feature extraction or model retraining. The evaluation results demonstrate that this approach performs on par with state-of-the-art machine learning-based methods.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Zhuyi Ni, Zexuan Ji, Long Lan, Yun-Hao Yuan, Xiaobo Shen
Summary: This letter presents a new approach of Unsupervised Discriminative Deep Hashing (UDH-H-2), which jointly performs hash code learning and clustering, trained in an asymmetric way to improve efficiency, effectively addressing the issue of lack of semantic supervision in deep unsupervised hashing. Experiments on three benchmark datasets demonstrate that UDH-H-2 outperforms state-of-the-art unsupervised deep hashing methods.
IEEE SIGNAL PROCESSING LETTERS
(2021)
Article
Mathematics
Mehdi Hosseinzadeh, Liliana Ionescu-Feleaga, Bogdan-Stefan Ionescu, Mahyar Sadrishojaei, Faeze Kazemian, Amir Masoud Rahmani, Faheem Khan
Summary: This study addresses the challenge of uneven power consumption in cluster-based routing in the Internet of Things environment by using swarm intelligence. By utilizing the firefly optimization method and the aquila optimizer algorithm, this approach significantly improves system energy usage and packet delivery ratio.
Article
Computer Science, Information Systems
Zhixia Zeng, Ruliang Xiao, Xinhong Lin, Tianjian Luo, Jiayin Lin
Summary: Most existing large-scale high-dimensional streaming anomaly detection methods have high time and space complexity and low generalization ability due to their sensitivity to parameters and limited applicability to specific scenarios. This paper proposes a three-layer structure high-dimensional streaming anomaly detection model called the double locality sensitive hashing Bloom filter (dLSHBF). The model consists of two layers of double locality sensitive hashing (dLSH) and a third layer of Bloom filter for improved efficiency. Comparative experiments using six large-scale high-dimensional data stream datasets demonstrate that the proposed dLSH algorithm has better distance-preserving performance than existing LSH algorithms, and the dLSHBF model is more efficient than other advanced Bloom filter models. Compared to state-of-the-art methods, dLSHBF achieves a detection rate (DR) of over 97% and a false alarm rate (FAR) of less than 2.2%, demonstrating its superior effectiveness and generalization ability in streaming anomaly detection.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Interdisciplinary Applications
Kamran Rezaei, Hassan Rezaei
Summary: The research introduces an improved firefly algorithm called INEFA, which enhances performance in solving optimization problems by introducing a new attraction model and clustering concept for fireflies.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Information Systems
Ya Liu, Yingjie Zhou, Kai Yang, Xin Wang
Summary: Internet of Things (IoT) time-series analysis has been widely used in various fields, but the complexity and high dimensionality of IoT time series make the analysis challenging. Deep learning has provided an effective method for IoT time-series analysis with its powerful feature extraction and representation learning capabilities. However, there are few existing surveys on unsupervised DL-based methods. In this study, we investigate unsupervised DL for IoT time series under a unified framework, including unsupervised anomaly detection and clustering, as well as discussing application scenarios, public data sets, existing challenges, and future research directions.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
J. Fumanal-Idocin, I Rodriguez-Martinez, A. Indurain, M. Minarova, H. Bustince
Summary: This paper proposes a simulation-based anomaly detection algorithm that identifies abnormal observations significantly different from normal ones using the aggregation of gravitational forces and cluster analysis, without prior knowledge or data labels.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Shaohua Huang, Yu Guo, Nengjun Yang, Shanshan Zha, Daoyuan Liu, Weiguang Fang
Summary: This study proposes a clustering method based on density peak-weighted fuzzy C-means for abnormal detection in production process using IoT data, which improves accuracy and convergence speed through feature reduction and clustering model construction.
JOURNAL OF INTELLIGENT MANUFACTURING
(2021)
Article
Telecommunications
Sankar Sennan, Somula Ramasubbareddy, Sathiyabhama Balasubramaniyam, Anand Nayyar, Chaker Abdelaziz Kerrache, Muhammad Bilal
Summary: The study introduces a dynamic clustering-based routing protocol called MADCR, utilizes mobility awareness and mayfly optimization algorithm to enhance the performance of Internet of Vehicles systems. MADCR protocol reduces end-to-end delay and increases packet delivery ratio.
CHINA COMMUNICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jiawei Yang, Susanto Rahardja, Pasi Franti
Summary: The mean-shift outlier detector modifies data using mean-shift technique to eliminate the bias caused by outliers and remove their influence without needing to know the outliers. Experimental results show that this method performs well regardless of the number of outliers in the data.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Twinkle Tiwari, Mukesh Saraswat
Summary: This paper presents a firefly algorithm-based superpixel clustering method for vehicle segmentation in crowded and unstructured road traffic images. The proposed method incorporates the best solution to enhance the firefly algorithm and achieves good segmentation results on a traffic dataset.
Review
Computer Science, Information Systems
Mehdi Hosseinzadeh, Atefeh Hemmati, Amir Masoud Rahmani
Summary: The development of internet of things (IoT) applications, particularly in smart cities, has been rapid. Clustering is a promising solution for addressing IoT issues, such as energy efficiency, scalability, robustness, and mobility. This study examines the usage of clustering in IoT through a case study on smart cities, discusses clustering algorithms, open issues, and future challenges, and reviews existing research papers published between 2017 and 2021. A technical taxonomy for clustering categorization in IoT is provided, including algorithm, architecture, and application. Analysis of selected research articles indicates that the number of clusters, energy factor, execution time, accuracy, delay, lifetime, and throughput are important factors in clustering in IoT.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Fan Wang, Min Zhu, Maoli Wang, Mohammad R. Khosravi, Qiang Ni, Shui Yu, Lianyong Qi
Summary: With the rise of IoT and intelligent transportation systems, the use of sensing devices on roads has increased for real-time traffic monitoring. Analyzing big traffic data in IoT can help traffic administrations make informed decisions to prevent future traffic congestions. This article proposes a big data-driven model aided by 6G for accurate short-term traffic flow prediction in massive IoT, showing increased prediction accuracy and efficiency compared to other approaches.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Himanshu Mittal, Ashish Kumar Tripathi, Avinash Chandra Pandey, Mohammad Dahman Alshehri, Mukesh Saraswat, Raju Pal
Summary: This paper presents a new clustering method for intrusion detection in the Industrial Internet-of-Things, and validates its efficacy through experimental analysis on benchmark functions and Industrial Internet-of-Things datasets. The proposed method outperforms existing methods in terms of F-measure and computation time, ensuring security in a real-time Industrial Internet-of-Things environment.
COMPUTER COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Hao Yu, Hui Chen, Shengjie Zhao, Qingjiang Shi
Summary: This article introduces a distributed soft clustering algorithm for data analysis in IoT networks, which addresses challenges such as data volume and information security. Through experiments, the algorithm is shown to perform as well as centralized methods, offering stable clustering quality and practical applications. The algorithm uses distributed deterministic initialization and finite-time average-consensus algorithm for efficient computation and stability improvement.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Software Engineering
Ankur Gupta, Purnendu Prabhat, Sahil Sawhney, Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar, Mohammad Shabaz
Summary: Robotic Process Automation (RPA) is a new sub-domain of software-based automation that aims to alleviate tedious manual and repetitive tasks. It finds wide adoption across industries and domains, including academia and document processing. While RPA offers potential benefits in terms of efficient data collation and analysis, as well as operational efficiency, its long-term seamless operation requires significant software engineering effort.
Article
Computer Science, Information Systems
Hakam Singh, Vipin Rai, Neeraj Kumar, Pankaj Dadheech, Ketan Kotecha, Ganeshsree Selvachandran, Ajith Abraham
Summary: This study introduces an enhanced whale optimization algorithm (EWOA) for clustering problems. By incorporating the position update equations from the water wave optimization algorithm and adding tabu and neighbourhood search mechanisms, the algorithm improves the search space and accelerates the convergence rate. Experimental results demonstrate the applicability and feasibility of the enhancements and the superiority of the proposed EWOA clustering algorithm.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Nadia Mumtaz, Naveed Ejaz, Shabana Habib, Syed Muhammad Mohsin, Prayag Tiwari, Shahab S. Band, Neeraj Kumar
Summary: This paper discusses the generation of big video data in smart cities and focuses on violence detection using deep learning approaches. The paper provides an overview of deep sequence learning methods and localization strategies for violence detection. It also explores the initial image processing and machine learning-based violence detection literature and their advantages and disadvantages. Additionally, the paper discusses datasets and proposes future directions in the violence detection domain based on in-depth analysis of previous methods.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Review
Computer Science, Information Systems
Deepanshi, Ishan Budhiraja, Deepak Garg, Neeraj Kumar, Rohit Sharma
Summary: SARS-CoV-2 is an infected disease caused by one of the variants of Coronavirus which emerged in December 2019. It is declared a pandemic by WHO in March 2020. COVID-19 outbreak has put the world on a halt and is a major threat to the public health system. Despite of numerous efforts, precautions and vaccination the infection has grown rapidly in the world.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Theory & Methods
Prateek Chhikara, Rajkumar Tekchandani, Neeraj Kumar
Summary: The Internet of Things (IoT) is crucial for deploying a novel Artificial Intelligence (AI) model for both network and application management. However, using classical centralized learning algorithms in the IoT environment is challenging, given massively distributed private datasets. The paper proposes two adaptive approaches for making model training differentially private in a vertical federated environment.
Article
Computer Science, Theory & Methods
Anichur Rahman, Kamrul Hasan, Dipanjali Kundu, Md. Jahidul Islam, Tanoy Debnath, Shahab S. Band, Neeraj Kumar
Summary: The individual and integrated use of IoT, ICN, and FL in network-related scenarios has gained significant attention in the research community. FL addresses privacy and security issues in a decentralized manner, while ICN retrieves and stores content based on content names rather than addresses. The upcoming 6G networks are expected to support massive IoT devices, and this research highlights the potential of ICN for IoT applications. This study provides a comprehensive survey of FL, IoT, and ICN, and discusses their integration and future directions.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Engineering, Electrical & Electronic
Ashwin Verma, Pronaya Bhattacharya, Deepti Saraswat, Sudeep Tanwar, Neeraj Kumar, Ravi Sharma
Summary: Recently, UAVs have been used for COVID-19 vaccine distribution to address fake vaccine issues. The authors propose a blockchain-assisted UAV vaccine distribution scheme based on sixth-generation enhanced ultra-reliable low latency communication (6G-eRLLC). The scheme utilizes a public Solana blockchain setup for user registration, vaccine request, and distribution, ensuring scalable transactions. With an intelligent edge offloading scheme, UAV swarms are deployed to deliver vaccines to nodal centers, showing significant improvements in service latency, energy reduction, UAV coverage, and storage cost compared to 5G uRLLC communication and Ethereum network.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Hardware & Architecture
Dan Tang, Xiyin Wang, Xiong Li, Pandi Vijayakumar, Neeraj Kumar
Summary: Low-rate denial of service (LDoS) attacks exploit network protocol vulnerabilities to launch periodic bursts, severely impacting TCP application quality of service. Current coarse-scale detection methods are ineffective. To accurately detect LDoS attacks, an adaptive Kohonen Network based fine-grained detection (AKN-FGD) model is proposed. The AKN-FGD scheme achieves accurate detection with high detection performance and adaptability, outperforming other methods.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Guozhi Liu, Fei Dai, Xiaolong Xu, Xiaodong Fu, Wanchun Dou, Neeraj Kumar, Muhammad Bilal
Summary: This paper proposes an adaptive DNN inference acceleration framework that utilizes end-edge-cloud collaborative computing to accelerate inference latency. The framework includes a latency prediction model and a computation partitioning algorithm, and experimental results show significant improvements in prediction accuracy and inference latency reduction.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jerry W. Sangma, Yogita, Vipin Pal, Neeraj Kumar, Riti Kushwaha
Summary: This article proposes a fuzzy hierarchical clustering method for clustering multiple nominal data streams using the clustering-by-variable approach. The method calculates the fuzzy affinity of data streams to different clusters using normalized cosine similarity and updates the hierarchical clustering structure based on changes in node entropy. Experimental results show that the proposed method outperforms other methods in terms of cluster quality and has great potential in capturing fuzzy clusters.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Yajie Wang, Yu-an Tan, Thar Baker, Neeraj Kumar, Quanxin Zhang
Summary: Industry 5.0 aims to merge the cognitive computing capabilities of DNNs with human resourcefulness in collaborative operations. However, DNNs are vulnerable to adversarial attacks, bringing risks to Industrial AIoT systems. To solve these problems, we propose two novel deep fusion methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Rajat Chaudhary, Neeraj Kumar
Summary: Software-Defined Internet of Vehicles (SD-IoV) is an emerging technology used in modern intelligent transportation systems. The goal of SD-IoV is to provide seamless connectivity with low latency and high-speed data transfer. However, the challenges of high power consumption and secure data transfer arise due to the increased density of connected vehicles using the Internet. In this paper, a joint power optimization and secrecy ensured scheme known as SecGreen is proposed to address these issues.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Sharnil Pandya, Hemant Ghayvat, Praveen Kumar Reddy, Thippa Reddy Gadekallu, Muhammad Ahmed Khan, Neeraj Kumar
Summary: In the current pandemic, global issues have caused both health problems and economic decline. Lockdown is the most effective measure to reduce the spread of the virus and save lives at the initial stage of a novel virus outbreak. The proposed COUNTERSAVIOR system utilizes Artificial Intelligence of Medical Things (AIoMT), edge line computing, and big data analytics to trace and track virus transmission using GPS data. The system aims to be a scientific tool for handling any virus outbreak, with the ability to identify alternative paths to prevent infections. Machine learning and deep learning methodologies are used to analyze historical location data and forecast behavior patterns of confirmed and suspected individuals. The system provides a report on virus exposure and available pandemic saviour paths.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Tauheed Ahmed, Shabnam Samima, Mohd Zuhair, Hemant Ghayvat, Muhammad Ahmed Khan, Neeraj Kumar
Summary: Safeguards against illegitimate access and identification are necessary in the Internet of Medical Things (IoMT) domain. Existing user identification schemes struggle with impersonation attacks, leaving systems vulnerable. This study explores the use of multimodal biometrics, specifically fingerprint and iris modalities, to develop an identification and access control system for the healthcare ecosystem.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Ashish Singh, Kakali Chatterjee, Anish Kumar Singh, Neeraj Kumar
Summary: Mobile-edge computing (MEC) is a new architecture providing services at the network edge, with potential applications in healthcare for remote patient monitoring, diagnosis, and treatment. However, there are security and privacy concerns related to remote data access, including unauthorized access and data leakage, which can make the system inconvenient, untrusted, less suitable, and vulnerable.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)