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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 76–100 of 311 papers

TitleStatusHype
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond—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
A Theorem of the Alternative for Personalized Federated Learning—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
Decentralized Personalized Federated Learning—0
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
Advances and Challenges in Meta-Learning: A Technical Review—0
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
Federated Neural Compression Under Heterogeneous Data—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
Asynchronous Personalized Federated Learning through Global Memorization—0
Decentralized Directed Collaboration for Personalized Federated Learning—0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning—0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism—0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning—0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks—0
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning—0
A Parameter Aggregation Strategy on Personalized Federated Learning—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
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