FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning

4 June 2021

Papers citing "FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning"

9 / 9 papers shown

Title
Collaborative Fairness in Federated Learning Lingjuan Lyu Xinyi Xu Qian Wang FedML 68 192 0 27 Aug 2020
Learning to Detect Malicious Clients for Robust Federated Learning Suyi Li Yong Cheng Wei Wang Yang Liu Tianjian Chen AAML FedML 109 225 0 01 Feb 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 256 6,261 0 10 Dec 2019
Data Valuation using Reinforcement Learning Jinsung Yoon Sercan O. Arik Tomas Pfister TDI 83 180 0 25 Sep 2019
Measure Contribution of Participants in Federated Learning Guan Wang Charlie Xiaoqian Dang Ziye Zhou FedML 93 200 0 17 Sep 2019
An Empirical Study of Example Forgetting during Deep Neural Network Learning Mariya Toneva Alessandro Sordoni Rémi Tachet des Combes Adam Trischler Yoshua Bengio Geoffrey J. Gordon 115 734 0 12 Dec 2018
No Peek: A Survey of private distributed deep learning Praneeth Vepakomma Tristan Swedish Ramesh Raskar O. Gupta Abhimanyu Dubey SyDa FedML 57 100 0 08 Dec 2018
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 213 2,894 0 14 Mar 2017
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 306 4,646 0 18 Oct 2016