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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 61–70 of 311 papers

TitleStatusHype
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated LearningCode0
Influence-oriented Personalized Federated Learning—0
Personalized Federated Learning for Generative AI-Assisted Semantic Communications—0
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients—0
Personalized Quantum Federated Learning for Privacy Image Classification—0
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration—0
Personalized Federated Learning via Backbone Self-Distillation—0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization—0
TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based ClusteringCode0
Personalized Federated Learning Techniques: Empirical Analysis—0
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