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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 5160 of 311 papers

TitleStatusHype
FedTP: Federated Learning by Transformer PersonalizationCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated LearningCode1
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical DatasetsCode1
Parameterized Knowledge Transfer for Personalized Federated LearningCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Personalized Federated Learning with First Order Model OptimizationCode1
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT ImagingCode1
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learningCode0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
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