Mingsen Mo¹, Shan Ji², Xiaowan Wang^3,*, Ghulam Mohiuddin⁴, Yongjun Ren¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 37-53, 2023, DOI:10.32604/cmc.2023.028843 - 22 September 2022

Abstract Due to the extensive use of various intelligent terminals and the popularity of network social tools, a large amount of data in the field of medical emerged. How to manage these massive data safely and reliably has become an important challenge for the medical network community. This paper proposes a data management framework of medical network community based on Consortium Blockchain (CB) and Federated learning (FL), which realizes the data security sharing between medical institutions and research institutions. Under this framework, the data security sharing mechanism of medical network community based on smart contract and the… More >

Darpan Majumder^1,*, S. Mohan Kumar²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1201-1217, 2023, DOI:10.32604/csse.2023.024868 - 15 June 2022

Abstract Edge computing is a cloud computing extension where physical computers are installed closer to the device to minimize latency. The task of edge data centers is to include a growing abundance of applications with a small capability in comparison to conventional data centers. Under this framework, Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence (AI) model without actually revealing the underlying data, which is significantly enhanced in terms of privacy. Federated learning (FL) is a recently developed decentralized… More >

Jiale Cheng¹, Zilong Jin^1,2,*

Journal on Internet of Things, Vol.4, No.3, pp. 183-195, 2022, DOI:10.32604/jiot.2022.042054 - 12 June 2023

Abstract A novel Federated learning classifier is proposed using the Dempster-Shafer (DS) theory for the set-valued classification of industrial IoT Distributed Denial of Service (DDoS) attack traffic. The proposed classifier, referred to as the evidence-based federated learning classifier, employs convolution and pooling layers to extract high-dimensional features of Distributed Denial of Service (DDoS) traffic from the local data of private industrial clients. The characteristics obtained from the various participants are transformed into mass functions and amalgamated utilizing Dempster’s rule within the DS layer, situated on the federated server. Lastly, the set value classification task of attack More >

Jie Deng¹, Ran Guo², Zilong Jin^1,3,*

Journal on Internet of Things, Vol.4, No.3, pp. 141-153, 2022, DOI:10.32604/jiot.2022.038914 - 12 June 2023

Abstract Traditional based deep learning intrusion detection methods face problems such as insufficient cloud storage, data privacy leaks, high communication costs, unsatisfactory detection rates, and false positive rate. To address existing issues in intrusion detection, this paper presents a novel approach called CS-FL, which combines Federated Learning and a Self-Attention Fusion Convolutional Neural Network. Federated Learning is a new distributed computing model that enables individual training of client data without uploading local data to a central server. at the same time, local training results are uploaded and integrated across all participating clients to produce a global… More >

Weimin He, Lei Zhao^*

Journal of New Media, Vol.4, No.4, pp. 191-203, 2022, DOI:10.32604/jnm.2022.032994 - 12 December 2022

Abstract Accurate electricity forecasting is the key basis for guiding the power sector to arrange operation plans and guaranteeing the profitability of electric power companies. However, with the increasing demand of enterprises and departments for data security, the phenomenon of “Isolated Data Island” becomes more and more serious, resulting in the accuracy loss of the traditional electricity prediction model. Federated learning, as an emerging artificial intelligence technology, is designed to ensure data privacy while carrying out efficient machine learning, which provides a new way to solve the problem of “Isolated Data Island” in terms of electricity… More >

Ibrahim Abunadi¹, Maha M. Althobaiti², Fahd N. Al-Wesabi^3,4, Anwer Mustafa Hilal⁵, Mohammad Medani⁶, Manar Ahmed Hamza^5,*, Mohammed Rizwanullah⁵, Abu Serwar Zamani⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1195-1212, 2022, DOI:10.32604/cmc.2022.025473 - 24 February 2022

Abstract The evolving “Industry 4.0” domain encompasses a collection of future industrial developments with cyber-physical systems (CPS), Internet of things (IoT), big data, cloud computing, etc. Besides, the industrial Internet of things (IIoT) directs data from systems for monitoring and controlling the physical world to the data processing system. A major novelty of the IIoT is the unmanned aerial vehicles (UAVs), which are treated as an efficient remote sensing technique to gather data from large regions. UAVs are commonly employed in the industrial sector to solve several issues and help decision making. But the strict regulations… More >

Navod Neranjan Thilakarathne¹, G. Muneeswari², V. Parthasarathy³, Fawaz Alassery⁴, Habib Hamam⁵, Rakesh Kumar Mahendran⁶, Muhammad Shafiq^7,*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 157-172, 2022, DOI:10.32604/iasc.2022.023763 - 05 January 2022

Abstract Healthcare is one of the notable areas where the integration of the Internet of Things (IoT) is highly adopted, also known as the Medical IoT (MIoT). So far, MIoT is revolutionizing healthcare because it provides many advantages for the benefit of patients and healthcare personnel. The use of MIoT is becoming a booming trend, generating a large amount of IoT data, which requires proper analysis to infer meaningful information. This has led to the rise of deploying artificial intelligence (AI) technologies, such as machine learning (ML) and deep learning (DL) algorithms, to learn the meaning… More >

Prohim Tam¹, Sa Math¹, Ahyoung Lee², Seokhoon Kim^1,3,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3319-3335, 2022, DOI:10.32604/cmc.2022.023215 - 07 December 2021

Abstract Federated learning (FL) activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging processes. However, in large-scale heterogeneous Internet of Things (IoT) cellular networks, massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled significantly. This paper introduces the system model of converging software-defined networking (SDN) and network functions virtualization (NFV) to enable device/resource abstractions and provide NFV-enabled edge FL (eFL) aggregation servers for advancing automation and controllability. Multi-agent deep Q-networks (MADQNs) target to enforce a… More >

Zhe Sun¹, Jiyuan Feng¹, Lihua Yin^1,*, Zixu Zhang², Ran Li¹, Yu Hu¹, Chongning Na³

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1867-1886, 2022, DOI:10.32604/cmc.2022.022290 - 03 November 2021

Abstract Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data. However, the training mechanism for passing model parameters is still threatened by gradient inversion, inference attacks, etc. With a lightweight encryption overhead, function encryption is a viable secure aggregation technique in federation learning, which is often used in combination with differential privacy. The function encryption in federal learning still has the following problems: a) Traditional function encryption usually requires a trust third party (TTP) to assign the keys. If a TTP colludes with a server, the… More >

Zhipin Gu¹, Liangzhong He², Peiyan Li¹, Peng Sun³, Jiangyong Shi¹, Yuexiang Yang^1,*

Computer Systems Science and Engineering, Vol.39, No.3, pp. 307-320, 2021, DOI:10.32604/csse.2021.017969 - 12 August 2021

Abstract Federated learning is an ideal solution to the limitation of not preserving the users’ privacy information in edge computing. In federated learning, the cloud aggregates local model updates from the devices to generate a global model. To protect devices’ privacy, the cloud is designed to have no visibility into how these updates are generated, making detecting and defending malicious model updates a challenging task. Unlike existing works that struggle to tolerate adversarial attacks, the paper manages to exclude malicious updates from the global model’s aggregation. This paper focuses on Byzantine attack and backdoor attack in… More >