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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 301–310 of 311 papers

TitleStatusHype
Personalized Federated Learning with Multiple Known ClustersCode0
Personalized Federated Learning with Server-Side InformationCode0
Personalized Multi-tier Federated LearningCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature AugmentationCode0
UPFL: Unsupervised Personalized Federated Learning towards New ClientsCode0
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series DataCode0
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP OptimizationCode0
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed DataCode0
Privacy-preserving patient clustering for personalized federated learningCode0
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