most citedFedGH: Heterogeneous Federated Learning with Generalized Global Header

2 citations · 3 across the 4 of their papers we have counts for

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cs.LG2023

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

Liping Yi, Han Yu, Zhuan Shi +4

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model…

cs.LG2023★ 1 cited

Fairness-Aware Client Selection for Federated Learning

Yuxin Shi, Zelei Liu, Zhuan Shi +1

Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients) to train machine learning models collaboratively without revealing private data. Since the FL server ca…

cs.LG2023★ 2 cited

FedGH: Heterogeneous Federated Learning with Generalized Global Header

Liping Yi, Gang Wang, Xiaoguang Liu +2

Federated learning (FL) is an emerging machine learning paradigm that allows multiple parties to train a shared model collaboratively in a privacy-preserving manner. Existing horiz…