Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication

9 February 2021

Papers citing "Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication"

5 / 5 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 14 1 0 01 Jun 2024
A Survey on Federated Analytics: Taxonomy, Enabling Techniques, Applications and Open Issues Zibo Wang Haichao Ji Yifei Zhu Dan Wang Zhu Han 43 1 0 19 Apr 2024
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off Yuecheng Li Lele Fu Tong Wang Jian Lou Bin Chen Lei Yang Zibin Zheng Zibin Zheng Chuan Chen FedML 65 4 0 10 Feb 2024
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks Natalie Lang Nir Shlezinger Rafael G. L. DÓliveira S. E. Rouayheb FedML 50 4 0 01 Aug 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 24 22 0 20 Jul 2023