How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning

IEEE Transactions on Dependable and Secure Computing(2022)

引用 43|浏览47
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摘要
This article first considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens and local credibility to ensure fairness, in combination with differential privacy to guarantee privacy. In particular, we build a fair and differentially private decentralised deep learning framework called FDPDDL, which...
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关键词
Deep learning,Servers,Privacy,Data models,Collaboration,Computational modeling,Data privacy
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