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 23, 2022

Personalized Federated Learning with Server-Side Information
Jaehun Song, Min-hwan Oh, Hyung-Sin Kim
Personalized Federated Learning Full Information Personalization Performance Meta Gradient Order Gradient

May 5, 2022

Uncertainty Minimization for Personalized Federated Semi-Supervised Learning
Yanhang Shi, Siguang Chen, Haijun Zhang
Pseudo Label Personalized Federated Learning Decentralized Learning Confidence Interval Model Personalization

April 28, 2022

Personalized Federated Learning with Multiple Known Clusters
Boxiang Lyu, Filip Hanzely, Mladen Kolar
High Similarity Personalized Federated Learning Different Cluster Cluster Structure Model Weight

March 25, 2022

Sparse Federated Learning with Hierarchical Personalized Models
Xiaofeng Liu, Qing Wang, Yunfeng Shao, Yinchuan Li
Personalized Federated Learning Personalized Model FedAvg Algorithm Proximal Gradient Descent Sparse Federated

March 2, 2022

Personalized Federated Learning With Graph
Fengwen Chen, Guodong Long, Zonghan Wu, Tianyi Zhou, Jing Jiang
Graph Drawing Personalized Federated Learning Model Personalization

February 20, 2022

Personalized Federated Learning with Exact Stochastic Gradient Descent
Sotirios Nikoloutsopoulos, Iordanis Koutsopoulos, Michalis K. Titsias
Stochastic Gradient Descent Personalized Federated Learning Unbiased Gradient

February 18, 2022

PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks
Xingjian Cao, Gang Sun, Hongfang Yu, Mohsen Guizani
Generative Adversarial Network Personalized Federated Learning C Gan Personalized Model Federated Training

February 6, 2022

Energy-Aware Edge Association for Cluster-based Personalized Federated Learning
Y. Li, X. Qin, H. Chen, K. Han, P. Zhang
Resource Allocation Personalized Federated Learning Edge Network Ubiquitous Intelligence Edge Association

February 5, 2022

A Coalition Formation Game Approach for Personalized Federated Learning
Leijie Wu, Song Guo, Yaohong Ding, Yufeng Zhan, Jie Zhang
Shapley Value Personalized Federated Learning Coalition Game

February 1, 2022

January 18, 2022

How to Backdoor HyperNetwork in Personalized Federated Learning?
Phung Lai, NhatHai Phan, Issa Khalil, Abdallah Khreishah, Xintao Wu
Personalized Federated Learning Non Graph Hypernetwork Baseline Model Stealing Attack Model Attack Gradient Attack

December 2, 2021

Personalized Federated Learning of Driver Prediction Models for Autonomous Driving
Manabu Nakanoya, Junha Im, Hang Qiu, Sachin Katti, Marco Pavone, Sandeep Chinchali
Autonomous Driving Autonomous Vehicle Personalized Federated Learning Driver Behavior Trajectory Prediction Model Robust Robot

December 1, 2021

Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Wang Lu, Jindong Wang, Yiqiang Chen, Xin Qin, Renjun Xu, Dimitrios Dimitriadis, Tao Qin
Healthcare System Personalized Federated Learning Batch Normalization Layer Medical Benchmark Batchnorm Minus Implementation

November 17, 2021

Personalized Federated Learning through Local Memorization
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal
Deep Neural Network Jina Embeddings Generalization Bound Personalized Federated Learning

November 16, 2021

On-Demand Unlabeled Personalized Federated Learning
Ohad Amosy, Gal Eyal, Gal Chechik
Differential Privacy Personalized Federated Learning Personalized Model Unlabeled Client

November 4, 2021