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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 51–60 of 311 papers

TitleStatusHype
Personalized Federated Learning via Feature Distribution AdaptationCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
Personalized Federated Learning with Feature Alignment and Classifier CollaborationCode1
Personalized Federated Learning with First Order Model OptimizationCode1
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-TuningCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Personalized Federated Continual Learning via Multi-granularity PromptCode1
PFL-MoE: Personalized Federated Learning Based on Mixture of ExpertsCode1
Agnostic Personalized Federated Learning with Kernel Factorization—0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
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