Towards Personalized Federated Learning via Heterogeneous Model
Reassembly

Towards Personalized Federated Learning via Heterogeneous Model Reassembly

16 August 2023

Dongkuan Xu

Papers citing "Towards Personalized Federated Learning via Heterogeneous Model Reassembly"

10 / 10 papers shown

Title
Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation Yongqian Li Bo Liu Sheng Huang Zhe Zhang Xiaotong Yuan Richang Hong 60 0 0 31 Mar 2025
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions Weiming Zhuang Chen Chen Lingjuan Lyu Chong Chen Yaochu Jin Lingjuan Lyu AIFin AI4CE 102 93 0 27 Jun 2023
FedGH: Heterogeneous Federated Learning with Generalized Global Header Liping Yi Gang Wang Xiaoguang Liu Zhuan Shi Han Yu FedML 68 75 0 23 Mar 2023
Git Re-Basin: Merging Models modulo Permutation Symmetries Samuel K. Ainsworth J. Hayase S. Srinivasa MoMe 264 326 0 11 Sep 2022
FedKD: Communication Efficient Federated Learning via Knowledge Distillation Chuhan Wu Fangzhao Wu Lingjuan Lyu Yongfeng Huang Xing Xie FedML 36 383 0 30 Aug 2021
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients Enmao Diao Jie Ding Vahid Tarokh FedML 63 550 0 03 Oct 2020
Federated Model Distillation with Noise-Free Differential Privacy Lichao Sun Lingjuan Lyu FedML 39 106 0 11 Sep 2020
FedMD: Heterogenous Federated Learning via Model Distillation Daliang Li Junpu Wang FedML 61 845 0 08 Oct 2019
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 498 36,599 0 25 Aug 2016
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 187 43,511 0 17 Sep 2014