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 161–170 of 311 papers

TitleStatusHype
UPFL: Unsupervised Personalized Federated Learning towards New ClientsCode0
You Can Backdoor Personalized Federated LearningCode1
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space—0
Privacy-preserving patient clustering for personalized federated learningCode0
Advances and Challenges in Meta-Learning: A Technical Review—0
Personalized Federated Learning via Amortized Bayesian Meta-Learning—0
FedCP: Separating Feature Information for Personalized Federated Learning via Conditional PolicyCode4
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
Personalized Federated Learning with Feature Alignment and Classifier CollaborationCode1
Provably Personalized and Robust Federated LearningCode0
Show:102550
← PrevPage 17 of 32Next →

No leaderboard results yet.