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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 251–275 of 311 papers

TitleStatusHype
Personalized Federated Learning with Communication Compression—0
Personalized Federated Learning with Attention-based Client Selection—0
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer—0
Personalized Federated Learning with Exact Stochastic Gradient Descent—0
Learn What You Need in Personalized Federated LearningCode0
Sparse Personalized Federated LearningCode0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
Federated Representation Learning in the Under-Parameterized RegimeCode0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Personalized Federated Learning via Heterogeneous Modular NetworksCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based ClusteringCode0
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated LearningCode0
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser SchemeCode0
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated LearningCode0
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic SurgeryCode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Provably Personalized and Robust Federated LearningCode0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language ModelsCode0
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
Adaptive Expert Models for Personalization in Federated LearningCode0
Flow: Per-Instance Personalized Federated Learning Through Dynamic RoutingCode0
Personalized Federated Learning via StackingCode0
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