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 181–190 of 311 papers

TitleStatusHype
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision—0
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation—0
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space—0
Tensor Decomposition based Personalized Federated Learning—0
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation—0
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning—0
Towards Generalizable Personalized Federated Learning with Adaptive Local Adaptation—0
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients—0
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training—0
Towards Personalized Federated Learning—0
Show:102550
← PrevPage 19 of 32Next →

No leaderboard results yet.