Yuwei Xu, Baokang Zhao^*, Huan Zhou, Jinshu Su

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 609-629, 2024, DOI:10.32604/cmes.2024.053462 - 20 August 2024

Abstract The rapid expansion of artificial intelligence (AI) applications has raised significant concerns about user privacy, prompting the development of privacy-preserving machine learning (ML) paradigms such as federated learning (FL). FL enables the distributed training of ML models, keeping data on local devices and thus addressing the privacy concerns of users. However, challenges arise from the heterogeneous nature of mobile client devices, partial engagement of training, and non-independent identically distributed (non-IID) data distribution, leading to performance degradation and optimization objective bias in FL training. With the development of 5G/6G networks and the integration of cloud computing… More >

Qiuyan Wang, Haibing Dong^*, Yongfei Huang, Zenglei Liu, Yundong Gou

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1967-1983, 2024, DOI:10.32604/cmc.2024.052775 - 15 August 2024

Abstract Sharing data while protecting privacy in the industrial Internet is a significant challenge. Traditional machine learning methods require a combination of all data for training; however, this approach can be limited by data availability and privacy concerns. Federated learning (FL) has gained considerable attention because it allows for decentralized training on multiple local datasets. However, the training data collected by data providers are often non-independent and identically distributed (non-IID), resulting in poor FL performance. This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain… 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