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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

Showing 151160 of 311 papers

TitleStatusHype
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning0
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated LearningCode2
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical DatasetsCode1
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations0
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond0
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning0
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated LearningCode4
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated LearningCode1
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
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