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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 251–300 of 311 papers

TitleStatusHype
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning—0
FedMCSA: Personalized Federated Learning via Model Components Self-Attention—0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification—0
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Find Your Friends: Personalized Federated Learning with the Right Collaborators—0
Federated Asymptotics: a model to compare federated learning algorithms—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
Group Personalized Federated Learning—0
Group privacy for personalized federated learning—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
Hierarchical Over-the-Air FedGradNorm—0
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks—0
How To Prevent the Poor Performance Clients for Personalized Federated Learning?—0
Incentivizing Inclusive Contributions in Model Sharing Markets—0
Inference-Time Personalized Federated Learning—0
On-Demand Unlabeled Personalized Federated Learning—0
Influence-oriented Personalized Federated Learning—0
Integrating Personalized Federated Learning with Control Systems for Enhanced Performance—0
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data—0
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning—0
Lower Bounds and Optimal Algorithms for Personalized Federated Learning—0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning—0
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning—0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning—0
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM—0
How to Backdoor HyperNetwork in Personalized Federated Learning?—0
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics—0
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data—0
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning—0
On Data Efficiency of Meta-learning—0
On Heterogeneously Distributed Data, Sparsity Matters—0
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization—0
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity—0
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks—0
PersA-FL: Personalized Asynchronous Federated Learning—0
Personalization Disentanglement for Federated Learning: An explainable perspective—0
Personalized Federated Domain Adaptation for Item-to-Item Recommendation—0
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning—0
Personalized Federated Learning: A Meta-Learning Approach—0
Personalized federated learning based on feature fusion—0
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework—0
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer—0
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