FedGCN: Convergence-Communication Tradeoffs in Federated Training of
Graph Convolutional Networks

FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks

28 January 2022

Weizhao Jin

Carlee Joe-Wong

Papers citing "FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks"

7 / 7 papers shown

Title
Subgraph Federated Learning for Local Generalization Sungwon Kim Yoonho Lee Yunhak Oh Namkyeong Lee Sukwon Yun Junseok Lee Sein Kim Carl Yang Chanyoung Park FedML OOD 86 1 0 06 Mar 2025
Deal: Distributed End-to-End GNN Inference for All Nodes Shiyang Chen Xiang Song Vasiloudis Theodore Hang Liu GNN 50 0 0 04 Mar 2025
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges Wei Ju Siyu Yi Yifan Wang Zhiping Xiao Zhengyan Mao ... Senzhang Wang Xinwang Liu Xiao Luo Philip S. Yu Ming Zhang AI4CE 36 35 0 07 Mar 2024
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 45 104 0 16 May 2022
Federated Learning on Non-IID Data Silos: An Experimental Study Yue Liu Yiqun Diao Quan Chen Bingsheng He FedML OOD 89 946 0 03 Feb 2021
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs Binghui Wang Ang Li H. Li Yiran Chen 88 115 0 08 Dec 2020
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 259 3,239 0 24 Nov 2016