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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 241–250 of 311 papers

TitleStatusHype
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
Personalized Federated Learning with Server-Side InformationCode0
ActPerFL: Active Personalized Federated Learning—0
Personalized Federated Learning with Multiple Known ClustersCode0
Self-Aware Personalized Federated Learning—0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning—0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning—0
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge IntelligenceCode1
Personalized Federated Learning With GraphCode1
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