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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 301–311 of 311 papers

TitleStatusHype
Personalized Federated Domain Adaptation for Item-to-Item Recommendation—0
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning—0
Personalized Federated Learning: A Meta-Learning Approach—0
Personalized federated learning based on feature fusion—0
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework—0
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer—0
Personalized Federated Learning for Statistical Heterogeneity—0
Personalized Federated Learning for Generative AI-Assisted Semantic Communications—0
Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing—0
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching—0
Personalized Federated Learning for Cross-view Geo-localization—0
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