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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 3140 of 311 papers

TitleStatusHype
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
An Empirical Study of Personalized Federated LearningCode1
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated LearningCode1
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