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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 176–200 of 311 papers

TitleStatusHype
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated LearningCode0
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training—0
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
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
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Personalized Federated Learning under Mixture of DistributionsCode1
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
Personalized Federated Learning with Local Attention—0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
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
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
FedABC: Targeting Fair Competition in Personalized Federated Learning—0
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization GuaranteesCode1
Cross-Fusion Rule for Personalized Federated Learning—0
Revisiting Personalized Federated Learning: Robustness Against Backdoor AttacksCode0
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation—0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes—0
Personalized Semantics Excitation for Federated Image Classification—0
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