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 41–50 of 311 papers

TitleStatusHype
Personalized Federated Learning With GraphCode1
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
Personalized Federated Learning through Local MemorizationCode1
Parameterized Knowledge Transfer for Personalized Federated LearningCode1
Connecting Low-Loss Subspace for Personalized Federated LearningCode1
Federated Multi-Task Learning under a Mixture of DistributionsCode1
On Bridging Generic and Personalized Federated Learning for Image ClassificationCode1
Personalized Federated Learning with Gaussian ProcessesCode1
Personalized Federated Learning by Structured and Unstructured Pruning under Data HeterogeneityCode1
Personalized Federated Learning using HypernetworksCode1
Show:102550
← PrevPage 5 of 32Next →

No leaderboard results yet.