How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
IEEE Transactions on Dependable and Secure Computing(2022)
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...
更多查看译文
关键词
Deep learning,Servers,Privacy,Data models,Collaboration,Computational modeling,Data privacy
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要