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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 201–225 of 311 papers

TitleStatusHype
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space—0
Privacy-preserving patient clustering for personalized federated learningCode0
Advances and Challenges in Meta-Learning: A Technical Review—0
Personalized Federated Learning via Amortized Bayesian Meta-Learning—0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
Provably Personalized and Robust Federated LearningCode0
PeFLL: Personalized Federated Learning by Learning to LearnCode0
Personalization Disentanglement for Federated Learning: An explainable perspective—0
Personalized Federated Domain Adaptation for Item-to-Item Recommendation—0
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity—0
Federated Neural Compression Under Heterogeneous Data—0
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning—0
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training—0
A novel parameter decoupling approach of personalized federated learning for image analysis—0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
Collaborative Chinese Text Recognition with Personalized Federated Learning—0
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM—0
Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
IP-FL: Incentivized and Personalized Federated Learning—0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
Personalized Federated Learning with Local Attention—0
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature AugmentationCode0
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks—0
Visual Prompt Based Personalized Federated Learning—0
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