SOTAVerified

Personalized Federated Learning

The federated learning setup presents numerous challenges including data heterogeneity (differences in data distribution), device heterogeneity (in terms of computation capabilities, network connection, etc.), and communication efficiency. Especially data heterogeneity makes it hard to learn a single shared global model that applies to all clients. To overcome these issues, Personalized Federated Learning (PFL) aims to personalize the global model for each client in the federation.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 281–290 of 311 papers

TitleStatusHype
Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application—0
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning—0
Sparse Personalized Federated LearningCode0
Personalized Federated Learning over non-IID Data for Indoor Localization—0
On Bridging Generic and Personalized Federated Learning for Image ClassificationCode1
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach—0
Personalized Federated Learning with Gaussian ProcessesCode1
Personalized Federated Learning with Contextualized Generalization—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
Show:102550
← PrevPage 29 of 32Next →

No leaderboard results yet.