Toward Understanding the Influence of Individual Clients in Federated
Learning

Toward Understanding the Influence of Individual Clients in Federated Learning

20 December 2020

Papers citing "Toward Understanding the Influence of Individual Clients in Federated Learning"

9 / 9 papers shown

Title
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Ljubomir Rokvic Panayiotis Danassis Boi Faltings FedML 96 0 0 05 May 2025
Quantifying Privacy Leakage in Split Inference via Fisher-Approximated Shannon Information Analysis Ruijun Deng Zhihui Lu Qiang Duan FedML 108 0 0 14 Apr 2025
FetchSGD: Communication-Efficient Federated Learning with Sketching D. Rothchild Ashwinee Panda Enayat Ullah Nikita Ivkin Ion Stoica Vladimir Braverman Joseph E. Gonzalez Raman Arora FedML 48 363 0 15 Jul 2020
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang Qinghua Liu Hao Liang Gauri Joshi H. Vincent Poor MoMe FedML 43 1,321 0 15 Jul 2020
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability Yikai Yan Chaoyue Niu Yucheng Ding Zhenzhe Zheng Fan Wu Guihai Chen Shaojie Tang Zhihua Wu FedML 138 38 0 18 Feb 2020
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning Sai Praneeth Karimireddy Satyen Kale M. Mohri Sashank J. Reddi Sebastian U. Stich A. Suresh FedML 47 345 0 14 Oct 2019
On the Accuracy of Influence Functions for Measuring Group Effects Pang Wei Koh Kai-Siang Ang H. Teo Percy Liang TDI 59 186 0 30 May 2019
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 104 1,410 0 03 Dec 2018
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 139 2,854 0 14 Mar 2017