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Blockchain-Enabled Federated Learning for Privacy-Preserving Non-IID Data Sharing in Industrial Internet
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作者 Qiuyan Wang Haibing Dong +2 位作者 yongfei huang Zenglei Liu Yundong Gou 《Computers, Materials & Continua》 SCIE EI 2024年第8期1967-1983,共17页
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 d... 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 technology.To overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local models.This approach can significantly improve the accuracy of FL training when there is relatively large dispersion.In addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model parameters.Finally,we evaluate the performance of the proposed scheme using commonly used open-source image datasets.The simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet. 展开更多
关键词 Federated learning data sharing non-IID data differential privacy blockchain
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Design strategy and bioimaging of small organic molecule multicolor fluorescent probes 被引量:6
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作者 yongfei huang Yongbin Zhang +2 位作者 Fangjun Huo Ying Wen Caixia Yin 《Science China Chemistry》 SCIE EI CAS CSCD 2020年第12期1742-1755,共14页
Multicolor fluorescent probes based on small organic molecules have the advantages of low cost, good biocompatibility, easily modifiable molecular structures and adjustable fluorescence performance. In addition, small... Multicolor fluorescent probes based on small organic molecules have the advantages of low cost, good biocompatibility, easily modifiable molecular structures and adjustable fluorescence performance. In addition, small molecule multicolor fluorescent probes generally undergo multi-site or multi-step reactions, which means that they can be used for the specific detection of structurally similar substances in complex bio-systems. In this review, we focus on the design and application of multicolor fluorescent probes based on small organic molecules: single fluorophores with multiple reaction sites, multiple fluorophores with single reaction sites, or multiple fluorophores with multiple reaction sites. Moreover, a design strategy for multicolor fluorescent probes and its application in biological imaging are also summarized, providing a systematic plan for future research on fluorescent probes functionalized by small organic molecules. It will also play an important role in the development of additional functions for small organic molecule fluorescent probes. 展开更多
关键词 MULTICOLOR fluorescence probes small organic molecules design strategy BIOIMAGING
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