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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 76–100 of 311 papers

TitleStatusHype
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas—0
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic SurgeryCode0
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical RepresentationsCode0
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation—0
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning—0
Personalized Multi-tier Federated LearningCode0
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis—0
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation—0
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
Personalized Federated Continual Learning via Multi-granularity PromptCode1
Towards Personalized Federated Multi-Scenario Multi-Task Recommendation—0
Personalized federated learning based on feature fusion—0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
Regularizing and Aggregating Clients with Class Distribution for Personalized Federated LearningCode0
Decentralized Personalized Federated Learning—0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning—0
Federated Representation Learning in the Under-Parameterized RegimeCode0
Sheaf HyperNetworks for Personalized Federated Learning—0
Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity—0
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP OptimizationCode0
Decentralized Directed Collaboration for Personalized Federated Learning—0
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