A Scalable Approach for Privacy-Preserving Collaborative Machine
Learning

A Scalable Approach for Privacy-Preserving Collaborative Machine Learning

3 November 2020

Papers citing "A Scalable Approach for Privacy-Preserving Collaborative Machine Learning"

9 / 9 papers shown

Title
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 45 23 0 20 Jul 2023
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing Jiawei Shao Yuchang Sun Songze Li Jun Zhang OOD 44 38 0 06 Oct 2022
FedMCSA: Personalized Federated Learning via Model Components Self-Attention Qianling Guo Yong Qi Saiyu Qi Di Wu Qian Li FedML 21 9 0 23 Aug 2022
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards Sebastian Shenghong Tay Xinyi Xu Chuan-Sheng Foo Bryan Kian Hsiang Low SyDa 24 32 0 17 Dec 2021
What Do We Mean by Generalization in Federated Learning? Honglin Yuan Warren Morningstar Lin Ning K. Singhal OOD FedML 43 71 0 27 Oct 2021
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges S. Ulukus Salman Avestimehr Michael C. Gastpar S. Jafar Ravi Tandon Chao Tian FedML 35 64 0 30 Jul 2021
Towards Industrial Private AI: A two-tier framework for data and model security Sunder Ali Khowaja K. Dev N. Qureshi P. Khuwaja L. Foschini FedML 18 5 0 27 Jul 2021
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning Jinhyun So Başak Güler A. Avestimehr FedML 27 289 0 11 Feb 2020
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning Jinhyun So Başak Güler A. Avestimehr FedML 22 114 0 02 Feb 2019