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
On Bridging Generic and Personalized Federated Learning for Image ClassificationCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
Federated Multi-Task Learning under a Mixture of DistributionsCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data DistributionCode1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT ImagingCode1
Show:102550
← PrevPage 5 of 32Next →

No leaderboard results yet.