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

July 17, 2024

Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation
Hang Chen, Collin Meese, Mark Nejad, Chien-Chung Shen
Personalized Federated Learning Traffic Prediction Traffic Forecasting Traffic Management Traffic Network Traffic Pattern Robust Aggregation

June 29, 2024

pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
Luyuan Xie, Manqing Lin, Siyuan Liu, ChenMing Xu, Tianyu Luan, Cong Li, Yuejian Fang, Qingni Shen, Zhonghai Wu
Medical Image Segmentation Segmentation Mask Personalized Federated Learning Feature Enhancement Medical Image Segmentation Task Segmentation Quality

June 28, 2024

Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
Xinghao Wu, Xuefeng Liu, Jianwei Niu, Haolin Wang, Shaojie Tang, Guogang Zhu, Hao Su
Data Heterogeneity Personalized Federated Learning Microbial Decomposition Multi Agent Decoupling Coefficient Personalized Model Client Collaboration Personalized Knowledge Feed Additive

June 27, 2024

Personalized Federated Continual Learning via Multi-granularity Prompt
Hao Yu, Xin Yang, Xin Gao, Yan Kang, Hao Wang, Junbo Zhang, Tianrui Li
Personalized Federated Learning Federated Continual Learning Hierarchical Prompting

June 18, 2024

Low-Resource Machine Translation through the Lens of Personalized Federated Learning
Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann, Samuel Horváth, Eduard Gorbunov, Irina Nikishina
Camera Lens Personalized Federated Learning Multilingual Machine Translation Multilingual Benchmark

June 12, 2024

Regularizing and Aggregating Clients with Class Distribution for Personalized Federated Learning
Gyuejeong Lee, Daeyoung Choi
Personalized Federated Learning Federated Averaging Neural Network Weight Class Distribution Client Clustering

June 10, 2024

May 31, 2024

May 29, 2024

FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization
Fan Zhang, Carlos Esteve-Yagüe, Sören Dittmer, Carola-Bibiane Schönlieb, Michael Roberts
Personalized Federated Learning Local Model Bi Level Optimization Unlocking Potential Maximum a Posteriori Personalized Fl

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

Personalized Federated Learning

Papers

Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation

pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation

Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition

Personalized Federated Continual Learning via Multi-granularity Prompt

Low-Resource Machine Translation through the Lens of Personalized Federated Learning

Regularizing and Aggregating Clients with Class Distribution for Personalized Federated Learning

Decentralized Personalized Federated Learning

Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning

Sheaf HyperNetworks for Personalized Federated Learning

Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity

FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization

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