FedDD: Toward Communication-Efficient Federated Learning With Differential Parameter Dropout - researchr publication related

researchr

You are not signed in
Sign in
Sign up

Zhiying Feng, Xu Chen 0004, Qiong Wu 0009, Wen Wu 0003, Xiaoxi Zhang, Qianyi Huang. FedDD: Toward Communication-Efficient Federated Learning With Differential Parameter Dropout. IEEE Trans. Mob. Comput., 23(5):5366-5384, May 2024. [doi]

The following publications are possibly variants of this publication:

Communication-efficient Federated Learning Framework with Parameter-Ordered DropoutQichen Li, Sujie Shao, Chao Yang, Jiewei Chen, Feng Qi 0004, Shaoyong Guo. cscwd 2024: 1195-1200 [doi]

Communication-Efficient Federated Learning with Adaptive Parameter FreezingChen Chen, Hong Xu, Wei Wang 0030, Baochun Li, Bo Li 0001, Li Chen, Gong Zhang. icdcs 2021: 1-11 [doi]

Two Birds With One Stone: Toward Communication and Computation Efficient Federated LearningYi Kong, Wei Yu, Shu Xu, Fei Yu 0003, Yinfei Xu, Yongming Huang 0001. icl, 28(9):2106-2110, September 2024. [doi]

To Distill or Not to Distill: Toward Fast, Accurate, and Communication-Efficient Federated Distillation LearningYuan Zhang 0013, Wenlong Zhang, Lingjun Pu, Tao Lin, Jinyao Yan. iotj, 11(6):10040-10053, March 2024. [doi]

DROPFL: Client Dropout Attacks Against Federated Learning Under Communication ConstraintsWenjun Qian, Qingni Shen, Haoran Xu, Xi Huang, Zhonghai Wu. icassp 2024: 4870-4874 [doi]

runs on WebDSL