9 citations · 28 across the 19 of their papers we have counts for
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cs.LG2022
Diversity Boosted Learning for Domain Generalization with Large Number of Domains
Xi Leng, Xiaoying Tang, Yatao Bian
Machine learning algorithms minimizing the average training loss usually suffer from poor generalization performance due to the greedy exploitation of correlations among the traini…
cs.LG2022★ 8 cited
DELTA: Diverse Client Sampling for Fasting Federated Learning
Lin Wang, YongXin Guo, Tao Lin +1
Partial client participation has been widely adopted in Federated Learning (FL) to reduce the communication burden efficiently. However, an inadequate client sampling scheme can le…
cs.LG2022★ 9 cited
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
Yongxin Guo, Xiaoying Tang, Tao Lin
Federated Learning (FL) is a way for machines to learn from data that is kept locally, in order to protect the privacy of clients. This is typically done using local SGD, which hel…