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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

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Showing 151–175 of 311 papers

TitleStatusHype
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning—0
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 Representations—0
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond—0
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning—0
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
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
PeFLL: Personalized Federated Learning by Learning to LearnCode0
Personalization Disentanglement for Federated Learning: An explainable perspective—0
Personalized Federated Domain Adaptation for Item-to-Item Recommendation—0
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity—0
Federated Neural Compression Under Heterogeneous Data—0
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