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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 101–110 of 311 papers

TitleStatusHype
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data—0
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated LearningCode0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser SchemeCode0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification—0
Personalized Federated Learning via StackingCode0
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