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

January 18, 2024

Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks
Yichao Du, Zhirui Zhang, Linan Yue, Xu Huang, Yuqing Zhang, Tong Xu, Linli Xu, Enhong Chen
Automatic Speech Recognition Speech Translation Personalized Federated Learning Speech to Text Whisper Model

January 17, 2024

FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data
Zikai Xiao, Zihan Chen, Liyinglan Liu, Yang Feng, Jian Wu, Wanlu Liu, Joey Tianyi Zhou, Howard Hao Yang, Zuozhu Liu
Federated Learning Personalized Federated Learning Neural Collapse Long Tailed Data Joint Framework Data Balancing Individual Classifier

January 16, 2024

December 23, 2023

December 20, 2023

Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity
Yiyue Chen, Haris Vikalo, Chianing Wang
Self Supervised Learning New Framework Data Heterogeneity Personalized Federated Learning Centralized Machine Learning Low Bit Quantization Learning Model Re Quantization

December 18, 2023

Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks
Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takáč, Maxim Panov
Federated Learning Real World Personalized Federated Learning Predictive Uncertainty Global Model Personalized Model Posterior Network

December 8, 2023

PFLlib: Personalized Federated Learning Algorithm Library
Jianqing Zhang, Yang Liu, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Jian Cao
Federated Learning Personalized Federated Learning pFL Method

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 Moreau Envelope Lipschitz Gradient

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

Personalized Federated Learning

Papers

Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks

FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data

Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach

Learn What You Need in Personalized Federated Learning

Personalized Federated Learning with Contextual Modulation and Meta-Learning

Personalized Federated Learning with Attention-based Client Selection

Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity

Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks

PFLlib: Personalized Federated Learning Algorithm Library

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