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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 261–270 of 311 papers

TitleStatusHype
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving—0
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization—0
Personalized Federated Learning through Local MemorizationCode1
A Parameter Aggregation Strategy on Personalized Federated Learning—0
On-Demand Unlabeled Personalized Federated Learning—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
Parameterized Knowledge Transfer for Personalized Federated LearningCode1
WAFFLE: Weighted Averaging for Personalized Federated Learning—0
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision—0
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