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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 126–150 of 311 papers

TitleStatusHype
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning—0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification—0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
Agnostic Personalized Federated Learning with Kernel Factorization—0
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models—0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization—0
FedMCSA: Personalized Federated Learning via Model Components Self-Attention—0
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning—0
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning—0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks—0
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning—0
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates—0
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