Xiaojun Jin¹, Chao Ma^2,*, Song Luo², Pengyi Zeng¹, Yifei Wei¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2317-2336, 2024, DOI:10.32604/cmc.2024.055344 - 18 November 2024

Abstract Federated learning enables data owners in the Internet of Things (IoT) to collaborate in training models without sharing private data, creating new business opportunities for building a data market. However, in practical operation, there are still some problems with federated learning applications. Blockchain has the characteristics of decentralization, distribution, and security. The blockchain-enabled federated learning further improve the security and performance of model training, while also expanding the application scope of federated learning. Blockchain has natural financial attributes that help establish a federated learning data market. However, the data of federated learning tasks may be… More >

Mohammad Z. Masoud¹, Yousef Jaradat¹, Ahmad Manasrah², Mohammad Alia³, Khaled Suwais^4,*, Sally Almanasra⁴

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 515-532, 2024, DOI:10.32604/cmc.2023.044504 - 30 January 2024

Abstract This work carried out a measurement study of the Ethereum Peer-to-Peer (P2P) network to gain a better understanding of the underlying nodes. Ethereum was applied because it pioneered distributed applications, smart contracts, and Web3. Moreover, its application layer language “Solidity” is widely used in smart contracts across different public and private blockchains. To this end, we wrote a new Ethereum client based on Geth to collect Ethereum node information. Moreover, various web scrapers have been written to collect nodes’ historical data from the Internet Archive and the Wayback Machine project. The collected data has been… More >

Hongle Guo^1,2, Yingchi Mao^1,2,*, Xiaoming He^1,2, Benteng Zhang^1,2, Tianfu Pang^1,2, Ping Ping^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 383-403, 2024, DOI:10.32604/cmes.2023.031466 - 30 December 2023

Abstract Data sharing and privacy protection are made possible by federated learning, which allows for continuous model parameter sharing between several clients and a central server. Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate, but because the clients are independent, the central server cannot fully control their behavior. The central server has no way of knowing the correctness of the model parameters provided by each client in this round, so clients may purposefully or unwittingly submit anomalous data, leading to abnormal behavior, such as becoming… More >

Tariq Qayyum¹, Zouheir Trabelsi^1,*, Asadullah Tariq¹, Muhammad Ali², Kadhim Hayawi³, Irfan Ud Din⁴

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1739-1757, 2023, DOI:10.32604/cmc.2023.043684 - 29 November 2023

Abstract Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed… More >

Akshaykumar Meshram^1,2, Monia Hadj Alouane-Turki³, N. M. Wazalwar², Chandrashekhar Meshram^4,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5337-5353, 2023, DOI:10.32604/cmc.2023.037324 - 29 April 2023

Abstract Internet of Things (IoT) applications can be found in various industry areas, including critical infrastructure and healthcare, and IoT is one of several technological developments. As a result, tens of billions or possibly hundreds of billions of devices will be linked together. These smart devices will be able to gather data, process it, and even come to decisions on their own. Security is the most essential thing in these situations. In IoT infrastructure, authenticated key exchange systems are crucial for preserving client and data privacy and guaranteeing the security of data-in-transit (e.g., via client identification… More >

Yan Zeng^1,2,3, Siyuan Teng¹, Tian Xiang^4,*, Jilin Zhang^1,2,3, Yuankai Mu⁵, Yongjian Ren^1,2,3,*, Jian Wan^1,2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1047-1064, 2023, DOI:10.32604/cmes.2023.027226 - 23 April 2023

Abstract Federated learning is a distributed machine learning method that can solve the increasingly serious problem of data islands and user data privacy, as it allows training data to be kept locally and not shared with other users. It trains a global model by aggregating locally-computed models of clients rather than their raw data. However, the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global model. For this problem, we focus on the client selection with federated learning, which can affect the convergence performance of the… More > Graphic Abstract

Sriramulu Bojjagani¹, D. R. Denslin Brabin^2,*, K. Saravanan²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 805-819, 2022, DOI:10.32604/iasc.2022.023771 - 03 May 2022

Abstract The flow of information is a valuable asset for every company and its consumers, and Distributed Denial-of-Service (DDoS) assaults pose a substantial danger to this flow. If we do not secure security, hackers may steal information flowing across a network, posing a danger to a business and society. As a result, the most effective ways are necessary to deal with the dangers. A DDoS attack is a well-known network infrastructure assault that prevents servers from servicing genuine customers. It is necessary to identify and block a DDoS assault before it reaches the server in order… More >

Manjur Kolhar^*, Abdalla Alameen

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 1-9, 2021, DOI:10.32604/iasc.2021.014995 - 17 March 2021

Abstract The use of computer-based technologies by non-native Arabic-speaking teachers for teaching native Arabic-speaking students can result in higher learner engagement. In this study, a machine translation (MT) system is developed as a learning technology. The proposed system can be linked to a digital podium and projector to reduce multitasking. A total of 25 students from Prince Sattam Bin Abdulaziz University, Saudi Arabia participated in our experiment and survey related to the use of the proposed technology-enhanced MT system. An important reason for using this framework is to exploit the high service bandwidth (up to several… More >

Jieren Cheng¹, Jun Li^{2, *}, Naixue Xiong³, Meizhu Chen², Hao Guo², Xinzhi Yao²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2247-2262, 2020, DOI:10.32604/cmc.2020.011668 - 16 September 2020

Abstract Nowadays, as lightweight mobile clients become more powerful and widely used, more and more information is stored on lightweight mobile clients, user sensitive data privacy protection has become an urgent concern and problem to be solved. There has been a corresponding rise of security solutions proposed by researchers, however, the current security mechanisms on lightweight mobile clients are proven to be fragile. Due to the fact that this research field is immature and still unexplored in-depth, with this paper, we aim to provide a structured and comprehensive study on privacy protection using trusted execution environment… More >

Quan Gu¹, Lei Wang¹, Surong Huang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.120, No.3, pp. 471-489, 2019, DOI:10.32604/cmes.2019.05757

Abstract Peridynamics (PD) is a powerful method to simulate the discontinuous problems in civil engineering. However, it may take a lot of effort to implement the material constitutive models into PD program for solving a broad range of problems. OpenSees is an open source software which includes a versatile material library and has been widely used by researchers and engineers in civil engineering. In this context, the paper presents a simple but effective approach to integrate PD with OpenSees by using a Client-Server (CS) software integration technique, such that the existing material constitutive models in OpenSees… More >