CD $^2$ -pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning

8 April 2022

Yiqing Shen

Papers citing "CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning"

34 / 34 papers shown

Title
Federated Client-tailored Adapter for Medical Image Segmentation Guyue Hu Siyuan Song Yukun Kang Z. Yin Gangming Zhao Chenglong Li Jin Tang FedML MedIm 186 0 0 25 Apr 2025
Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation Yongqian Li Bo Liu Sheng Huang Z. Zhang Xiaotong Yuan Richang Hong 46 0 0 31 Mar 2025
Delayed Random Partial Gradient Averaging for Federated Learning Xinyi Hu FedML 43 0 0 31 Dec 2024
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis Jiaqi Wang Ziyi Yin Quanzeng You Lingjuan Lyu Fenglong Ma FedML 71 0 0 27 Dec 2024
Federated brain tumor segmentation: an extensive benchmark Matthis Manthe Stefan Duffner Carole Lartizien OOD FedML 43 4 0 07 Oct 2024
Tailored Federated Learning: Leveraging Direction Regulation & Knowledge Distillation Huidong Tang Chen Li Huachong Yu Sayaka Kamei Yasuhiko Morimoto FedML 25 0 0 29 Sep 2024
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator Kangyang Luo Shuai Wang Xiang Li Yunshi Lan Ming Gao Jinlong Shu FedML 30 1 0 11 Sep 2024
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery Jialang Xu Jiacheng Wang Lequan Yu Danail Stoyanov Yueming Jin E. Mazomenos 36 1 0 06 Aug 2024
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning Fengling Lv Xinyi Shang Yang Zhou Yiqun Zhang Mengke Li Yang Lu FedML 47 0 0 04 Aug 2024
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations Guogang Zhu Xuefeng Liu Jianwei Niu Shaojie Tang Xinghao Wu Jiayuan Zhang AI4CE 47 1 0 25 Jul 2024
FedPartWhole: Federated domain generalization via consistent part-whole hierarchies Ahmed Radwan Mohamed S. Shehata FedML 29 0 0 20 Jul 2024
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions Laiqiao Qin Tianqing Zhu Wanlei Zhou Philip S. Yu 43 5 0 16 Jun 2024
Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations Chuan Chen Tianchi Liao Xiaojun Deng Zihou Wu Sheng Huang Zibin Zheng FedML 41 1 0 16 May 2024
Federated Distillation: A Survey Lin Li Jianping Gou Baosheng Yu Lan Du Zhang Yiand Dacheng Tao DD FedML 56 4 0 02 Apr 2024
FedUV: Uniformity and Variance for Heterogeneous Federated Learning Ha Min Son M. Kim Tai-Myung Chung Chao Huang Xin Liu FedML 43 3 0 27 Feb 2024
Spectral Co-Distillation for Personalized Federated Learning Zihan Chen Howard H. Yang Tony Q. S. Quek Kai Fong Ernest Chong OOD FedML 29 13 0 29 Jan 2024
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data Zikai Xiao Zihan Chen Liyinglan Liu Yang Feng Jian Wu Wanlu Liu Qiufeng Wang Howard H. Yang Zuo-Qiang Liu FedML 39 6 0 17 Jan 2024
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning Zhongyi Cai Ye-ling Shi Wei Huang Jingya Wang FedML 32 4 0 21 Dec 2023
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data Feifei Wang Huiyun Tang Yang Li FedML 27 0 0 07 Dec 2023
UNIDEAL: Curriculum Knowledge Distillation Federated Learning Yuwen Yang Chang Liu Xun Cai Suizhi Huang Hongtao Lu Yue Ding FedML 42 8 0 16 Sep 2023
Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation Fu-En Yang Chien-Yi Wang Yu-Chiang Frank Wang VLM FedML 31 59 0 29 Aug 2023
Heterogeneous Federated Learning: State-of-the-art and Research Challenges Mang Ye Xiuwen Fang Bo Du PongChi Yuen Dacheng Tao FedML AAML 39 244 0 20 Jul 2023
A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation Ronast Subedi Rebati Gaire Sharib Ali Anh Totti Nguyen Danail Stoyanov Binod Bhattarai OOD 19 2 0 14 Jun 2023
FedWon: Triumphing Multi-domain Federated Learning Without Normalization Weiming Zhuang Lingjuan Lyu 19 9 0 09 Jun 2023
Federated Domain Generalization: A Survey Ying Li Xingwei Wang Rongfei Zeng Praveen Kumar Donta Ilir Murturi Min Huang Schahram Dustdar OOD FedML AI4CE 42 29 0 02 Jun 2023
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification Hong-You Chen Shitian Zhao Mingda Zhang Xuhui Jia Qi Boqing Gong Wei-Lun Chao Li Zhang FedML 30 6 0 16 Apr 2023
Robust Knowledge Adaptation for Federated Unsupervised Person ReID Jianfeng Weng Kun Hu Tingting Yao Jingya Wang Zhiyong Wang FedML 24 2 0 18 Jan 2023
One-Time Model Adaptation to Heterogeneous Clients: An Intra-Client and Inter-Image Attention Design Yikai Yan Chaoyue Niu Fan Wu Qinya Li Shaojie Tang Chengfei Lyu Guihai Chen 26 0 0 11 Nov 2022
Personalized Federated Learning with Communication Compression El Houcine Bergou Konstantin Burlachenko Aritra Dutta Peter Richtárik FedML 74 9 0 12 Sep 2022
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data Ha Min Son M. Kim T. Chung FedML 14 9 0 01 Dec 2021
Towards Personalized Federated Learning A. Tan Han Yu Li-zhen Cui Qiang Yang FedML AI4CE 209 840 0 01 Mar 2021
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization Xiaoxiao Li Meirui Jiang Xiaofei Zhang Michael Kamp Qi Dou OOD FedML 168 787 0 15 Feb 2021
Survey of Personalization Techniques for Federated Learning V. Kulkarni Milind Kulkarni Aniruddha Pant FedML 182 326 0 19 Mar 2020
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen Harri Valpola OOD MoMe 261 1,275 0 06 Mar 2017

CD2^22-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning

Papers citing "CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning"

CD $^2$ -pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning