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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 211–220 of 311 papers

TitleStatusHype
Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery—0
Personalized Federated Learning with Multi-branch Architecture—0
FedTP: Federated Learning by Transformer PersonalizationCode1
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism—0
Prototype-Based Layered Federated Cross-Modal Hashing—0
Personalized Federated Learning via Heterogeneous Modular NetworksCode0
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning—0
Find Your Friends: Personalized Federated Learning with the Right Collaborators—0
Group Personalized Federated Learning—0
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