PGFed: Personalize Each Client's Global Objective for Federated Learning

2 December 2022

Papers citing "PGFed: Personalize Each Client's Global Objective for Federated Learning"

8 / 8 papers shown

Title
Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models Jun Luo Cheng Chen Shandong Wu FedML VLM MoE 52 3 0 14 Oct 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
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning Jun-Jie Luo Shandong Wu OOD MQ 65 75 0 15 Oct 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 187 411 0 14 Jul 2021
Towards Personalized Federated Learning A. Tan Han Yu Li-zhen Cui Qiang Yang FedML AI4CE 209 840 0 01 Mar 2021
Adaptive Personalized Federated Learning Yuyang Deng Mohammad Mahdi Kamani M. Mahdavi FedML 212 542 0 30 Mar 2020
Survey of Personalization Techniques for Federated Learning V. Kulkarni Milind Kulkarni Aniruddha Pant FedML 182 326 0 19 Mar 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 332 11,684 0 09 Mar 2017