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

May 28, 2024

Decentralized Directed Collaboration for Personalized Federated Learning
Yingqi Liu, Yifan Shi, Qinglun Li, Baoyuan Wu, Xueqian Wang, Li Shen
Convergence Rate Personalized Federated Learning Decentralized Manner

May 25, 2024

FedSheafHN: Personalized Federated Learning on Graph-structured Data
Wenfei Liang, Yanan Zhao, Rui She, Yiming Li, Wee Peng Tay
Graph Neural Network Federated Learning Raw Data Personalized Federated Learning Model Personalization

May 7, 2024

pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving
Wei-Bin Kou, Qingfeng Lin, Ming Tang, Sheng Xu, Rongguang Ye, Yang Leng, Shuai Wang, Guofa Li, Zhenyu Chen, Guangxu Zhu, Yik-Chung Wu
Autonomous Driving Personalized Federated Learning Large Vision Model Intermediate Latent

April 27, 2024

April 24, 2024

Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme
Qianyu Long, Qiyuan Wang, Christos Anagnostopoulos, Daning Bi
Personalized Federated Learning Decentralized Federated Learning Hop Neighbor Sparse to Sparse Training

April 16, 2024

Personalized Federated Learning via Stacking
Emilio Cantu-Cervini
Strong Generalization Federated Learning Personalized Federated Learning Dual Domain Anti Personalization

April 4, 2024

Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach
Qingxiang Liu, Sheng Sun, Yuxuan Liang, Jingjing Xue, Min Liu
Personalized Federated Learning Spatio Temporal Forecasting Contrastive Alignment Spatiotemporal Pattern

April 3, 2024

FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
Rishub Tamirisa, Chulin Xie, Wenxuan Bao, Andy Zhou, Ron Arel, Aviv Shamsian
Many Parameter Personalized Federated Learning Client Data Heterogeneity

March 28, 2024

Client-supervised Federated Learning: Towards One-model-for-all Personalization
Peng Yan, Guodong Long
Personalized Federated Learning Personalized Model Personalized Fl

March 18, 2024

FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update
Ziru Niu, Hai Dong, A. K. Qin
Federated Learning Personalized Federated Learning Resource Constrained Device Parameter Update Network Bottleneck

March 14, 2024

DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning
Xu Yang, Jiyuan Feng, Songyue Guo, Ye Wang, Ye Ding, Binxing Fang, Qing Liao
Personalized Federated Learning Personalized Learning Affinity Score

February 29, 2024

RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection
Yang Xu, Yunlin Tan, Cheng Zhang, Kai Chi, Peng Sun, Wenyuan Yang, Ju Ren, Hongbo Jiang, Yaoxue Zhang
Personalized Federated Learning Watermarking Method Robust Watermark

February 25, 2024

Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune
Mengen Luo, Chi Xu, Ercan Engin Kuruoglu
Normalizing Flow Bayesian Neural Network Data Heterogeneity Personalized Federated Learning Personalized Model

February 19, 2024

Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
Kaan Ozkara, Bruce Huang, Ruida Zhou, Suhas Diggavi
Diffusion Model Data Heterogeneity Personalized Federated Learning Hierarchical Bayesian Personalized Interpretable

February 16, 2024

FedD2S: Personalized Data-Free Federated Knowledge Distillation
Kawa Atapour, S. Jamal Seyedmohammadi, Jamshid Abouei, Arash Mohammadi, Konstantinos N. Plataniotis
Knowledge Distillation Personalized Federated Learning Local Model Data Free Knowledge Distillation Federated Distillation

February 15, 2024

Prompt-based Personalized Federated Learning for Medical Visual Question Answering
He Zhu, Ren Togo, Takahiro Ogawa, Miki Haseyama
Data Heterogeneity Personalized Federated Learning Medical Datasets Medical Visual Question Answering Heterogeneous Clinical Trainable Parameter

February 7, 2024

Personalized Federated Learning for Statistical Heterogeneity
Muhammad Firdaus, Kyung-Hyune Rhee
Personalized Federated Learning Sensitive Data Data Privacy Statistical Heterogeneity Collaborative Model

January 29, 2024

Personalized Federated Learning

Papers

Decentralized Directed Collaboration for Personalized Federated Learning

FedSheafHN: Personalized Federated Learning on Graph-structured Data

pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving

FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data

Personalized Federated Learning via Sequential Layer Expansion in Representation Learning

Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme

Personalized Federated Learning via Stacking

Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach

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

Client-supervised Federated Learning: Towards One-model-for-all Personalization

FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update

DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning

RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection

Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune

Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning

FedD2S: Personalized Data-Free Federated Knowledge Distillation

Prompt-based Personalized Federated Learning for Medical Visual Question Answering

Personalized Federated Learning for Statistical Heterogeneity

Spectral Co-Distillation for Personalized Federated Learning

Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation