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 13, 2024

FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems
Mattia Giovanni Campana, Franca Delmastro
Personalized Federated Learning Feature Enhancement Heterogeneous Data User Behavior Wearable Device Pervasive Computing

November 7, 2024

Personalized Federated Learning for Cross-view Geo-localization
Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos
Federated Learning Personalized Federated Learning Cross View Cross View Geo Localization

November 6, 2024

Towards Personalized Federated Learning via Comprehensive Knowledge Distillation
Pengju Wang, Bochao Liu, Weijia Guo, Yong Li, Shiming Ge
Knowledge Distillation Mutual Distillation Personalized Federated Learning Personalized Model Personalized Knowledge

November 4, 2024

FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
Han Liang, Ziwei Zhan, Weijie Liu, Xiaoxi Zhang, Chee Wei Tan, Xu Chen
Personalized Federated Learning Client Collaboration Client Heterogeneity Personalized Federated

November 1, 2024

Personalized Federated Learning via Feature Distribution Adaptation
Connor J. Mclaughlin, Lili Su
Representation Learning Personalized Federated Learning Generative Task Learning Decomposition Feature Adaptation

October 28, 2024

Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
Pouya M. Ghari, Yanning Shen
Fine Tuning Full Model Human Prediction Adaptive Importance Personalized Federated Learning Fine Tuned Model Federated Setting

October 24, 2024

FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning
I-Cheng Lin, Osman Yagan, Carlee Joe-Wong
Personalized Federated Learning Decentralized FL Decentralized Manner Soft Clustering Decentralized Framework

October 19, 2024

Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer
Keting Yin, Jiayi Mao
Strong Generalization Federated Learning Knowledge Transfer Personalized Federated Learning Feature Aggregation Privacy Preserving Machine Learning Personalized Model

October 18, 2024

Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning
Boning Zhang, Dongzhu Liu, Osvaldo Simeone, Guanchu Wang, Dimitrios Pezaros, Guangxu Zhu
Uncertainty Quantification Personalized Federated Learning Confidence Estimation pFL Method

October 16, 2024

TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic
Jinqian Chen, Jihua Zhu
Native Robustness Personalized Federated Learning Instance Wise Uncertainty Subjective Logic

October 9, 2024

Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
Zhilong Li, Xiaohu Wu, Xiaoli Tang, Tiantian He, Yew-Soon Ong, Mengmeng Chen, Qiqi Liu, Qicheng Lao, Han Yu
Model Training Data Heterogeneity Personalized Federated Learning Evaluation Benchmark Statistical Heterogeneity Benchmark Framework

October 3, 2024

Personalized Federated Learning for Generative AI-Assisted Semantic Communications
Yubo Peng, Feibo Jiang, Li Dong, Kezhi Wang, Kun Yang
Generative Question Semantic Communication Personalized Federated Learning Generative Semantic Communication

September 24, 2024

Personalized Federated Learning via Backbone Self-Distillation
Pengju Wang, Bochao Liu, Dan Zeng, Chenggang Yan, Shiming Ge
Personalized Federated Learning Self Distillation Numerous Cutting Edge Backbone

September 20, 2024

DP$^2$-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization
Zhenxiao Zhang, Yuanxiong Guo, Yanmin Gong
Personalized Federated Learning Sharpness Aware Minimization Private Federated Learning Differentially Private Federated Learning

September 16, 2024

TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering
Rasoul Jafari Gohari, Laya Aliahmadipour, Ezat Valipour
Deep Learning Machine Learning Personalized Federated Learning Personalization Method

September 10, 2024

Personalized Federated Learning Techniques: Empirical Analysis
Azal Ahmad Khan, Ahmad Faraz Khan, Haider Ali, Ali Anwar
Adversarial Attack Multi Objective Personalized Federated Learning Empirical Analysis USER Provided Name

September 9, 2024

pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning
Jiahao Lai, Jiaqi Li, Jian Xu, Yanru Wu, Boshi Tang, Siqi Chen, Yongfeng Huang, Wenbo Ding, Yang Li
Diffusion Model Personalized Federated Learning Federated Averaging Parameter Selection

September 3, 2024

Personalized Federated Learning via Active Sampling
Alexander Jung, Yasmin SarcheshmehPour, Amirhossein Mohammadi
Personalized Federated Learning Data Generation Active Sampling Local Datasets

August 15, 2024

Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
Shunxin Guo, Hongsong Wang, Shuxia Lin, Zhiqiang Kou, Xin Geng
Personalized Federated Learning Heterogeneous Federated Learning Prototype Alignment Unknown Heterogeneity

August 11, 2024

Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas
Judith Sáinz-Pardo Díaz, María Castrillo, Juraj Bartok, Ignacio Heredia Cachá, Irina Malkin Ondík, Ivan Martynovskyi, Khadijeh Alibabaei, Lisana Berberi, Valentin Kozlov, Álvaro López García
Personalized Federated Learning Wave Over the Horizon Radar Radar Field Spatial Heterogeneity Precipitation Associated Weather Radar Radar Echo

Personalized Federated Learning

Papers

FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems

Personalized Federated Learning for Cross-view Geo-localization

Towards Personalized Federated Learning via Comprehensive Knowledge Distillation

FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients

Personalized Federated Learning via Feature Distribution Adaptation

Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning

FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning

Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer

Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning

TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic

Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning

Personalized Federated Learning for Generative AI-Assisted Semantic Communications

Personalized Federated Learning via Backbone Self-Distillation

DP$^2$-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization

TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering

Personalized Federated Learning Techniques: Empirical Analysis

pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning

Personalized Federated Learning via Active Sampling

Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization

Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas