17 citations · 31 across the 6 of their papers we have counts for
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cs.LG2024
WassFFed: Wasserstein Fair Federated Learning
Zhongxuan Han, Li Zhang, Chaochao Chen +4
Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training da…
cs.LG2023★ 17 cited
Integration of Large Language Models and Federated Learning
Chaochao Chen, Xiaohua Feng, Yuyuan Li +4
As the parameter size of Large Language Models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has…
cs.LG2023★ 8 cited
Federated Learning on Non-iid Data via Local and Global Distillation
Xiaolin Zheng, Senci Ying, Fei Zheng +4
Most existing federated learning algorithms are based on the vanilla FedAvg scheme. However, with the increase of data complexity and the number of model parameters, the amount of…