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

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

18 July 2020

Karthik Nandakumar

Jiangshan Yu

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

9 / 9 papers shown

Title
Data Quality in Edge Machine Learning: A State-of-the-Art Survey M. D. Belgoumri Mohamed Reda Bouadjenek Sunil Aryal Hakim Hacid 56 1 0 01 Jun 2024
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions Weiming Zhuang Chen Chen Lingjuan Lyu Chong Chen Yaochu Jin Lingjuan Lyu AIFin AI4CE 99 87 0 27 Jun 2023
A Survey on Federated Recommendation Systems Zehua Sun Yonghui Xu Yue Liu Weiliang He Lanju Kong Fangzhao Wu Yiheng Jiang Li-zhen Cui FedML 34 60 0 27 Dec 2022
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms Ehsan Hallaji R. Razavi-Far M. Saif AAML FedML 29 13 0 05 Jul 2022
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning A. Mondal Harpreet Virk Debayan Gupta 45 15 0 06 Feb 2022
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 357 0 07 Dec 2020
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning Xinyi Xu Lingjuan Lyu FedML 31 69 0 20 Nov 2020
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness Lingjuan Lyu Xuanli He Yitong Li 35 89 0 03 Oct 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 204 436 0 04 Mar 2020