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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
Adaptive Expert Models for Personalization in Federated LearningCode0
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health MonitoringCode0
Sparse Personalized Federated LearningCode0
pFL-Bench: A Comprehensive Benchmark for Personalized Federated LearningCode0
Personalized Federated Learning: A Unified Framework and Universal Optimization TechniquesCode0
Federated Representation Learning in the Under-Parameterized RegimeCode0
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature AugmentationCode0
PeFLL: Personalized Federated Learning by Learning to LearnCode0
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser SchemeCode0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
DAMe: Personalized Federated Social Event Detection with Dual Aggregation MechanismCode0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
Learn What You Need in Personalized Federated LearningCode0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
Flow: Per-Instance Personalized Federated Learning Through Dynamic RoutingCode0
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed DataCode0
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal HealthcareCode0
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic SurgeryCode0
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
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