177 citations · 611 across the 20 of their papers we have counts for
3 papers · 1 filter
VeriFi: Towards Verifiable Federated Unlearning
Xiangshan Gao, Xingjun Ma, Jingyi Wang +5
Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is…
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
Lingjuan Lyu, Yitong Li, Karthik Nandakumar +2
This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens a…
Towards Fair and Privacy-Preserving Federated Deep Models
Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar +5
The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a centra…