Analyzing Federated Learning with Enhanced Privacy Preservation

Sheng Po Tseng, Lo Yao Yeh, Lee Chi Wu, Pei Yu Tsai

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

For machine learning in online social networks or mobile environments, data privacy-preserving is a very important issue. In the past, artificial intelligence and machine learning algorithms that operating on a stand-alone machine must analyze each data in order to build an accurate model when training a model. However, this method is insufficient in terms of privacy protection, because it must collect data from different sources; on the other hand, if these algorithms analyze only a part of the data, the accuracy of the models constructed by artificial intelligence and machine learning algorithms will be very low. To solve the above problems, this study starts from the privacy protection aspect of industrial data management, combined with the concept of federated learning, and hopes to improve the accuracy of algorithm modules while ensuring data privacy. Therefore, in this paper, we analyze the impact of federated learning under different protocol mechanisms on the accuracy of algorithm modules, and based on this, we can apply this module to integrate blockchain smart contract technology in the future.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2022 23rd IEEE International Conference on Mobile Data Management, MDM 2022
發行者Institute of Electrical and Electronics Engineers Inc.
頁面446-451
頁數6
ISBN(電子)9781665451765
DOIs
出版狀態已出版 - 2022
事件23rd IEEE International Conference on Mobile Data Management, MDM 2022 - Virtual, Paphos, Cyprus
持續時間: 6 6月 20229 6月 2022

出版系列

名字Proceedings - IEEE International Conference on Mobile Data Management
2022-June
ISSN(列印)1551-6245

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???event.eventtypes.event.conference???23rd IEEE International Conference on Mobile Data Management, MDM 2022
國家/地區Cyprus
城市Virtual, Paphos
期間6/06/229/06/22

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