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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 101–110 of 311 papers

TitleStatusHype
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism—0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning—0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
A Parameter Aggregation Strategy on Personalized Federated Learning—0
A novel parameter decoupling approach of personalized federated learning for image analysis—0
FedD2S: Personalized Data-Free Federated Knowledge Distillation—0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
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