most citedFOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data

5 citations · 9 across the 4 of their papers we have counts for

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cs.LG2023★ 3 cited

PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees

Chulin Xie, De-An Huang, Wenda Chu +4

Personalized Federated Learning (pFL) has emerged as a promising solution to tackle data heterogeneity across clients in FL. However, existing pFL methods either (1) introduce high…

cs.LG2022★ 5 cited

FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data

Wenda Chu, Chulin Xie, Boxin Wang +5

Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to…