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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
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
An Empirical Study of Personalized Federated LearningCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Adaptive Test-Time Personalization for Federated LearningCode1
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
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