Personalized Fl

Personalized Federated Learning (PFL) aims to train customized machine learning models for individual users while preserving data privacy, addressing the challenge of data heterogeneity in standard Federated Learning. Current research focuses on developing efficient algorithms, such as those employing bi-level optimization, ADMM, and multi-branch architectures, to improve model accuracy and convergence speed while mitigating communication overhead. These advancements are significant because they enable the development of more accurate and robust machine learning models tailored to individual needs in various applications while maintaining user privacy.

Papers

July 25, 2024

Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets
Madapu Amarlingam, Abhishek Wani, Adarsh NL
Federated Learning Heterogeneous Data Client Model Personalized Fl Asset Class

July 23, 2024

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
Shengkun Zhu, Jinshan Zeng, Sheng Wang, Yuan Sun, Xiaodong Li, Yuan Yao, Zhiyong Peng
Native Robustness Procedural Fairness Heterogeneous Data Heterogeneous Federated Learning Alternating Direction Method of Multiplier Statistical Heterogeneity Superlinear Convergence Personalized Fl

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 Personalized Fl Maximum a Posteriori

April 20, 2024

Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training
Yebo Wu, Li Li, Chunlin Tian, Chengzhong Xu
Heterogeneous Federated Learning Progressive Alignment Progressive Training Personalized Fl Peak Memory

March 28, 2024

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

May 24, 2023

Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training
Yifan Shi, Yingqi Liu, Yan Sun, Zihao Lin, Li Shen, Xueqian Wang, Dacheng Tao
Model Training Decentralized FL Decentralized Training Model Personalization Personalized Fl

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

July 5, 2022

A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy
Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas Diggavi
Practical Algorithm Estimation Task Privacy Policy Generative Framework Personalized Learning Visual Estimation Personalized Fl

February 13, 2022

On the Convergence of Clustered Federated Learning
Jie Ma, Guodong Long, Tianyi Zhou, Jing Jiang, Chengqi Zhang
Early Stage Convergence Personalized Model Model Personalization Clustered Federated Learning Personalized Fl

Personalized Fl

Papers

Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness

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

Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training

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

Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training

Personalized Federated Learning with Multi-branch Architecture

A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy

On the Convergence of Clustered Federated Learning