Zheng Yang, Ke Gu^*, Yiming Zuo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2813-2831, 2024, DOI:10.32604/cmc.2024.050025 - 15 May 2024

Abstract Federated learning is widely used to solve the problem of data decentralization and can provide privacy protection for data owners. However, since multiple participants are required in federated learning, this allows attackers to compromise. Byzantine attacks pose great threats to federated learning. Byzantine attackers upload maliciously created local models to the server to affect the prediction performance and training speed of the global model. To defend against Byzantine attacks, we propose a Byzantine robust federated learning scheme based on backdoor triggers. In our scheme, backdoor triggers are embedded into benign data samples, and then malicious More >

Meenakshi Aggarwal¹, Vikas Khullar¹, Sunita Rani², Thomas André Prola^3,4,5, Shyama Barna Bhattacharjee⁶, Sarowar Morshed Shawon⁷, Nitin Goyal^8,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1795-1834, 2024, DOI:10.32604/cmc.2024.049846 - 15 May 2024

Abstract The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation. However, FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios. The paper systematically reviewed the available literature using the PRISMA guiding principle. The study aims to provide a detailed overview of the increasing use of FL in IoT networks, including the architecture and challenges. A systematic review approach is used to collect, categorize and analyze FL-IoT-based articles.… More >

Hao Sun^*, Xiubo Chen, Kaiguo Yuan

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2361-2373, 2024, DOI:10.32604/cmc.2024.049159 - 15 May 2024

Abstract Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data. However, there is still a potential risk of privacy leakage, for example, attackers can obtain the original data through model inference attacks. Therefore, safeguarding the privacy of model parameters becomes crucial. One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process. However, the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes. To solve the above… More >

Hina Batool¹, Jiuyun Xu^1,*, Ateeq Ur Rehman², Habib Hamam^3,4,5,6

Journal on Artificial Intelligence, Vol.6, pp. 105-128, 2024, DOI:10.32604/jai.2024.049912 - 06 May 2024

Abstract Federated Learning (FL) appeared as an encouraging approach for handling decentralized data. Creating a FL system needs both machine learning (ML) knowledge and thinking about how to design system software. Researchers have focused a lot on the ML side of FL, but have not paid enough attention to designing the software architecture. So, in this survey, a set of design patterns is described to tackle the design issues. Design patterns are like reusable solutions for common problems that come up when designing software architecture. This paper focuses on (1) design patterns such as architectures, frameworks,… More >

Mai Alzamel^1,*, Hamza Ali Rizvi², Najwa Altwaijry¹, Isra Al-Turaiki¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4177-4204, 2024, DOI:10.32604/cmc.2024.048370 - 26 March 2024

Abstract Scalability and information personal privacy are vital for training and deploying large-scale deep learning models. Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers. Nevertheless, relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers. Additionally, information relating to the training dataset can possibly be extracted from the distributed weights, potentially reducing the privacy of the local data used for training. In this research… More >

Yuming Tang^1,#, Yitian Zhang^2,#, Tao Niu¹, Zhen Li^2,3,*, Zijian Zhang^1,3, Huaping Chen⁴, Long Zhang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2451-2477, 2024, DOI:10.32604/cmes.2024.030084 - 11 March 2024

Abstract Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data… More >

Lalit Mohan Patnaik^1,5, Wenfeng Wang^2,3,4,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1203-1215, 2024, DOI:10.32604/cmes.2023.029451 - 29 January 2024

Abstract This article reviews the theory of fairness in AI–from machine learning to federated learning, where the constraints on precision AI fairness and perspective solutions are also discussed. For a reliable and quantitative evaluation of AI fairness, many associated concepts have been proposed, formulated and classified. However, the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure fairness. The privacy worries induce the data unfairness and hence, the biases in the datasets for evaluating AI fairness are unavoidable. The imbalance between algorithms’ utility and humanization More >

Sanxiu Jiao¹, Lecai Cai^2,*, Jintao Meng³, Yue Zhao³, Kui Cheng²

Computer Systems Science and Engineering, Vol.48, No.1, pp. 247-265, 2024, DOI:10.32604/csse.2023.040194 - 26 January 2024

Abstract Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (Efficient DP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while 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 >

Chunlin Xiong^1,2, Zhengqiu Weng^3,4,*, Jia Liu¹, Liang Gu², Fayez Alqahtani⁵, Amr Gafar⁶, Pradip Kumar Sharma⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3013-3032, 2024, DOI:10.32604/cmes.2023.031088 - 15 December 2023

Abstract With the development of hardware devices and the upgrading of smartphones, a large number of users save privacy-related information in mobile devices, mainly smartphones, which puts forward higher demands on the protection of mobile users’ privacy information. At present, mobile user authentication methods based on human-computer interaction have been extensively studied due to their advantages of high precision and non-perception, but there are still shortcomings such as low data collection efficiency, untrustworthy participating nodes, and lack of practicability. To this end, this paper proposes a privacy-enhanced mobile user authentication method with motion sensors, which mainly… More >