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Ten Challenging Problems in Federated Foundation Models

Ten Challenging Problems in Federated Foundation Models

14 February 2025
Tao Fan
Hanlin Gu
Xuemei Cao
Chee Seng Chan
Qian Chen
Yiqiang Chen
Yihui Feng
Yang Gu
Jiaxiang Geng
B. Luo
Shuoling Liu
W. Ong
Chao Ren
Jiaqi Shao
Chuan Sun
Xiaoli Tang
Hong Xi Tae
Yongxin Tong
Shuyue Wei
Fan Wu
Wei Xi
Mingcong Xu
H. Yang
Xin Yang
Jiangpeng Yan
H. Yu
Han Yu
Teng Zhang
Y. Zhang
Xiaojin Zhang
Zhenzhe Zheng
Lixin Fan
Qiang Yang
    FedML
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Papers citing "Ten Challenging Problems in Federated Foundation Models"

3 / 3 papers shown
Title
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization
Zhuang Qi
Sijin Zhou
Lei Meng
Han Hu
Han Yu
Xiangxu Meng
FedML
CML
142
0
0
08 May 2025
Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients
Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients
Leming Wu
Yaochu Jin
K. Hao
Han Yu
FedML
51
0
0
17 Apr 2025
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
Penghao Wang
Qian Chen
Teng Zhang
Y. Zhang
Wang Lu
Yiqiang Chen
25
0
0
15 Apr 2025
1