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

December 2, 2022

November 28, 2022

Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing
Kunjal Panchal, Sunav Choudhary, Nisarg Parikh, Lijun Zhang, Hui Guan
Fine Grained Personalized Federated Learning Dynamic Routing Flow Mood Personalized Model

November 19, 2022

Personalized Federated Learning with Hidden Information on Personalized Prior
Mingjia Shi, Yuhao Zhou, Qing Ye, Jiancheng Lv
Personalized Federated Learning Bayesian Learning Hidden Knowledge Bregman Divergence pFL Method Privacy Preserving Training Personalized Prior

November 17, 2022

Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery
Sheng Guo, Zengxiang Li, Hui Liu, Shubao Zhao, Cheng Hao Jin
Data Heterogeneity Personalized Federated Learning Robot Team Fault Diagnosis Industrial Machine Intelligent Fault Diagnosis

November 15, 2022

Personalized Federated Learning with Multi-branch Architecture
Junki Mori, Tomoyuki Yoshiyama, Furukawa Ryo, Isamu Teranishi
Personalized Federated Learning Decentralized Machine Learning Dual Branch Personalized Fl

October 26, 2022

Personalized Federated Learning via Heterogeneous Modular Networks
Tianchun Wang, Wei Cheng, Dongsheng Luo, Wenchao Yu, Jingchao Ni, Liang Tong, Haifeng Chen, Xiang Zhang
Personalized Federated Learning Heterogeneous Network Modular Network Heterogeneous GNN

October 25, 2022

FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks
Jaehee Jang, Heonseok Ha, Dahuin Jung, Sungroh Yoon
Deep Neural Network Personalized Federated Learning FedAvg Converges Class Weight Classification Layer Ensemble Averaging Heterogeneous Autoencoder

October 12, 2022

Find Your Friends: Personalized Federated Learning with the Right Collaborators
Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, Jesse C. Cresswell
Data Heterogeneity Personalized Federated Learning Expectation Maximization Global Model First Benchmark Dataset

September 27, 2022

Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks
Chaoqun You, Daquan Feng, Kun Guo, Howard H. Yang, Tony Q. S. Quek
Federated Learning Personalized Federated Learning Personalized Federated

September 24, 2022

Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning
Xin Yang, Omid Ardakanian
Personalized Federated Learning Sensor System

September 12, 2022

Personalized Federated Learning with Communication Compression
El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta, Peter Richtárik
Personalized Federated Learning Communication Compression Vanilla Gradient Descent Fast AltGDA Type Algorithm Bidirectional Compression

August 27, 2022

Tensor Decomposition based Personalized Federated Learning
Qing Wang, Jing Jin, Xiaofeng Liu, Huixuan Zong, Yunfeng Shao, Yinchuan Li
Federated Learning Personalized Federated Learning Tensor Decomposition Personalized Model

August 23, 2022

FedMCSA: Personalized Federated Learning via Model Components Self-Attention
Qi Guo, Yong Qi, Saiyu Qi, Di Wu, Qian Li
Machine Learning Model Self Attention Personalized Federated Learning

July 13, 2022

One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis
Onat Dalmaz, Usama Mirza, Gökberk Elmas, Muzaffer Özbey, Salman UH Dar, Emir Ceyani, Salman Avestimehr, Tolga Çukur
Full Model Personalized Federated Learning MRI Synthesis Source Target

June 27, 2022

An Empirical Study of Personalized Federated Learning
Koji Matsuda, Yuya Sasaki, Chuan Xiao, Makoto Onizuka
Federated Learning Empirical Study Data Heterogeneity Personalized Federated Learning

June 16, 2022

Personalized Federated Learning via Variational Bayesian Inference
Xu Zhang, Yinchuan Li, Wenpeng Li, Kaiyang Guo, Yunfeng Shao
Personalized Federated Learning Model Overfitting Variational Bayesian Inference

June 8, 2022

June 6, 2022

An Optimal Transport Approach to Personalized Federated Learning
Farzan Farnia, Amirhossein Reisizadeh, Ramtin Pedarsani, Ali Jadbabaie
Federated Learning Optimal Transport Personalized Federated Learning Multi Marginal Optimal Transport