ICML 2022spotlight111 citations
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, Xiaowen Chu
Abstract
In federated learning (FL), model performance typically suffers from client drift induced by data heterogeneity, and mainstream works focus on correcting client drift. We propose a different approach named virtual homogeneity learning (VHL) to directly “rectify” the data heterogeneity. In particular, VHL conducts FL with a virtual homogeneous dataset crafted to satisfy two conditions: containing
BibTeX
@InProceedings{pmlr-v162-tang22d,
title = {Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning},
author = {Tang, Zhenheng and Zhang, Yonggang and Shi, Shaohuai and He, Xin and Han, Bo and Chu, Xiaowen},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {21111--21132},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/tang22d/tang22d.pdf},
url = {https://proceedings.mlr.press/v162/tang22d.html},
abstract = {In federated learning (FL), model performance typically suffers from client drift induced by data heterogeneity, and mainstream works focus on correcting client drift. We propose a different approach named virtual homogeneity learning (VHL) to directly “rectify” the data heterogeneity. In particular, VHL conducts FL with a virtual homogeneous dataset crafted to satisfy two conditions: containing