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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 291–300 of 311 papers

TitleStatusHype
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics—0
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data—0
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning—0
On Data Efficiency of Meta-learning—0
On Heterogeneously Distributed Data, Sparsity Matters—0
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization—0
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity—0
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks—0
PersA-FL: Personalized Asynchronous Federated Learning—0
Personalization Disentanglement for Federated Learning: An explainable perspective—0
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