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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
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
A Theorem of the Alternative for Personalized Federated Learning—0
Towards Personalized Federated Learning—0
Personalized Federated Learning: A Unified Framework and Universal Optimization TechniquesCode0
On Data Efficiency of Meta-learning—0
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring—0
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach—0
Lower Bounds and Optimal Algorithms for Personalized Federated Learning—0
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization—0
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework—0
Personalized Federated Learning: A Meta-Learning Approach—0
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