Federated Learning from Pre-Trained Models: A Contrastive Learning
Approach

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

21 September 2022

Papers citing "Federated Learning from Pre-Trained Models: A Contrastive Learning Approach"

17 / 17 papers shown

Title
Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning Md. Tanzib Hosain Asif Zaman Md. Shahriar Sajid Shadman Sakeeb Khan Shanjida Akter 31 0 0 03 May 2025
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning Xingbo Fu Zihan Chen Yinhan He Song Wang Binchi Zhang Chen Chen Jundong Li OOD FedML 59 1 0 24 Feb 2025
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning Yanbing Zhou Xiangmou Qu Chenlong You Jiyang Zhou Jingyue Tang Xin Zheng Chunmao Cai Yingbo Wu FedML 50 1 0 09 Jan 2025
FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework Yuchen He Chuyun Shen Xiangfeng Wang Bo Jin FedML 23 1 0 04 Nov 2024
FedBaF: Federated Learning Aggregation Biased by a Foundation Model Jong-Ik Park Srinivasa Pranav J. M. F. Moura Carlee Joe-Wong AI4CE 68 2 0 24 Oct 2024
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security Youyang Qu Ming Liu Tianqing Zhu Longxiang Gao Shui Yu Wanlei Zhou MU FedML 52 2 0 14 Jun 2024
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation Li Wang Lei Sang Quangui Zhang Qiang Wu Min Xu 22 3 0 06 Mar 2024
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality Huy Q. Le Chu Myaet Thwal Yu Qiao Ye Lin Tun Minh N. H. Nguyen Choong Seon Hong Choong Seon Hong 52 3 0 25 Jan 2024
Rethinking Client Drift in Federated Learning: A Logit Perspective Yu-bao Yan Chun-Mei Feng Senior Member Ieee Wangmeng Zuo Senior Member Ieee Mang Ye Mong Goh Ping Li Rick Siow Lei Zhu F. I. C. L. Philip Chen FedML 16 8 0 20 Aug 2023
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions Weiming Zhuang Chen Chen Lingjuan Lyu C. L. P. Chen Yaochu Jin Lingjuan Lyu AIFin AI4CE 83 84 0 27 Jun 2023
FedSSC: Shared Supervised-Contrastive Federated Learning Sirui Hu Ling Feng Xiaohan Yang Yongchao Chen FedML 14 4 0 14 Jan 2023
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning Shangchao Su Min Yang Bin Li Xiangyang Xue VLM FedML 14 17 0 15 Nov 2022
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning John Nguyen Jianyu Wang Kshitiz Malik Maziar Sanjabi Michael G. Rabbat FedML AI4CE 13 21 0 30 Jun 2022
CalFAT: Calibrated Federated Adversarial Training with Label Skewness Chen Chen Yuchen Liu Xingjun Ma Lingjuan Lyu FedML 150 31 0 30 May 2022
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels Zhuowei Wang Tianyi Zhou Guodong Long Bo Han Jing Jiang FedML 14 11 0 20 May 2022
FedProc: Prototypical Contrastive Federated Learning on Non-IID data Xutong Mu Yulong Shen Ke Cheng Xueli Geng Jiaxuan Fu Tao Zhang Zhiwei Zhang FedML 30 161 0 25 Sep 2021
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization Xiaoxiao Li Meirui Jiang Xiaofei Zhang Michael Kamp Qi Dou OOD FedML 165 770 0 15 Feb 2021