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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 251–260 of 311 papers

TitleStatusHype
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions—0
Federated Neural Compression Under Heterogeneous Data—0
FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions—0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning—0
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring—0
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning—0
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning—0
FedMCSA: Personalized Federated Learning via Model Components Self-Attention—0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
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