Personalized Federated Learning

Personalized federated learning (PFL) aims to train customized machine learning models for individual clients in a distributed setting, preserving data privacy. Current research emphasizes efficient aggregation techniques, often employing backbone self-distillation, generative parameter aggregation, or class-wise averaging, to balance personalization with global knowledge sharing and address data heterogeneity. This field is significant because it enables the development of more accurate and adaptable models across diverse applications while maintaining user privacy, particularly relevant in healthcare, autonomous driving, and other sensitive data domains.

Papers

November 12, 2023

Personalized Federated Learning via ADMM with Moreau Envelope
Shengkun Zhu, Jinshan Zeng, Sheng Wang, Yuan Sun, Zhiyong Peng
Personalized Federated Learning Global Convergence Alternating Direction Method of Multiplier Superlinear Convergence Lipschitz Gradient Moreau Envelope

November 9, 2023

Personalized Online Federated Learning with Multiple Kernels
Pouya M. Ghari, Yanning Shen
Personalized Federated Learning Small Kernel MADDPG Algorithm Kernel Learning Multiple Kernel

November 8, 2023

Decentralized Personalized Online Federated Learning
Renzhi Wu, Saayan Mitra, Xiang Chen, Anup Rao
Personalized Federated Learning Local Model Personalized Model Recommendation Datasets

October 29, 2023

Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach
Zhou Ni, Morteza Hashemi
Multi Armed Bandit Personalized Federated Learning Confidence Bound Decentralized Training

October 27, 2023

Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning
Wenlong Deng, Christos Thrampoulidis, Xiaoxiao Li
Vision Transformer Fine Tuning Full Potential Personalized Federated Learning Bridging Text Pre Trained Vision Transformer Visual Prompt Tuning

October 22, 2023

PPFL: A Personalized Federated Learning Framework for Heterogeneous Population
Hao Di, Yi Yang, Haishan Ye, Xiangyu Chang
Personalized Federated Learning Personalization Method Heterogeneous Population Personalized Interpretable

October 13, 2023

PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Mingjia Shi, Yuhao Zhou, Kai Wang, Huaizheng Zhang, Shudong Huang, Qing Ye, Jiangcheng Lv
Reward Model Personalized Federated Learning Full Information Prior Knowledge Mirror Descent Bregman Divergence

October 8, 2023

ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
Wang Lu, Hao Yu, Jindong Wang, Damien Teney, Haohan Wang, Yiqiang Chen, Qiang Yang, Xing Xie, Xiangyang Ji
Personalized Federated Learning Zeroth Order Model Zoo

September 27, 2023

Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations
Hui Chen, Hengyu Liu, Longbing Cao, Tiancheng Zhang
Personalized Federated Learning Bayesian Perspective Hidden Representation Sharing Matter Uncertainty Representation Personalized Prior

September 22, 2023

Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients
Shourya Bose, Kibaek Kim
Personalized Federated Learning Energy Consumption Heterogeneous Client Short Term Load Forecasting Personalization Layer

September 20, 2023

Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration
Xinghao Wu, Xuefeng Liu, Jianwei Niu, Guogang Zhu, Shaojie Tang
Full Potential Personalized Federated Learning Estimated Team Strength Personalized Model Client Collaboration Efficient Collaboration Unknown Distribution

August 23, 2023

PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning
Achintha Wijesinghe, Songyang Zhang, Zhi Ding
Generative Model Generative Adversarial Network Personalized Federated Learning Client Heterogeneity Personalized Generative Client Data Heterogeneity

August 20, 2023

GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Jian Cao, Haibing Guan
World Event Personalized Federated Learning pFL Method Personalization System

August 17, 2023

FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning
Guangyu Sun, Matias Mendieta, Jun Luo, Shandong Wu, Chen Chen
Vision Transformer Personalized Federated Learning Decentralized Learning Self Attention Layer Model Personalization

August 16, 2023

Towards Personalized Federated Learning via Heterogeneous Model Reassembly
Jiaqi Wang, Xingyi Yang, Suhan Cui, Liwei Che, Lingjuan Lyu, Dongkuan Xu, Fenglong Ma
Personalized Federated Learning Heterogeneous Model Model Reuse Model Heterogeneity

July 29, 2023

UPFL: Unsupervised Personalized Federated Learning towards New Clients
Tiandi Ye, Cen Chen, Yinggui Wang, Xiang Li, Ming Gao
Personalized Federated Learning Personalized Model Client Collaboration Risk Minimization Knowledge Distillation Loss

July 26, 2023

Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space
Guogang Zhu, Xuefeng Liu, Shaojie Tang, Jianwei Niu, Xinghao Wu, Jiaxing Shen
Cross Domain Personalized Federated Learning Dimensional Feature Global Encoder Adaptive Pick Rule Personalization Layer

July 5, 2023

Personalized Federated Learning via Amortized Bayesian Meta-Learning
Shiyu Liu, Shaogao Lv, Dun Zeng, Zenglin Xu, Hui Wang, Yue Yu
Generalization Performance Personalized Federated Learning

July 1, 2023

FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy
Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan
Feature Wise Personalized Federated Learning

June 23, 2023

FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning
Rishub Tamirisa, John Won, Chengjun Lu, Ron Arel, Andy Zhou
Fine Tuning Many Parameter Personalized Federated Learning Global Aggregation

Personalized Federated Learning

Papers

Personalized Federated Learning via ADMM with Moreau Envelope

Personalized Online Federated Learning with Multiple Kernels

Decentralized Personalized Online Federated Learning

Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach

Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning

PPFL: A Personalized Federated Learning Framework for Heterogeneous Population

PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning

ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning

Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations

Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients

Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration

PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning

GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning

FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning

Towards Personalized Federated Learning via Heterogeneous Model Reassembly

UPFL: Unsupervised Personalized Federated Learning towards New Clients

Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space

Personalized Federated Learning via Amortized Bayesian Meta-Learning

FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy

FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning