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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 121–130 of 311 papers

TitleStatusHype
Federated Asymptotics: a model to compare federated learning algorithms—0
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning—0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
Cross-Fusion Rule for Personalized Federated Learning—0
ActPerFL: Active Personalized Federated Learning—0
How To Prevent the Poor Performance Clients for Personalized Federated Learning?—0
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