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 131–140 of 311 papers

TitleStatusHype
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical RepresentationsCode0
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation—0
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning—0
Personalized Multi-tier Federated LearningCode0
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation—0
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis—0
Towards Personalized Federated Multi-Scenario Multi-Task Recommendation—0
Personalized federated learning based on feature fusion—0
Show:102550
← PrevPage 14 of 32Next →

No leaderboard results yet.