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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 101–125 of 311 papers

TitleStatusHype
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature AugmentationCode0
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
Personalized Federated Learning via Heterogeneous Modular NetworksCode0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic SurgeryCode0
PeFLL: Personalized Federated Learning by Learning to LearnCode0
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant ClientsCode0
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical RepresentationsCode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Provably Personalized and Robust Federated LearningCode0
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal TransportCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Learn What You Need in Personalized Federated LearningCode0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
Personalized Federated Learning with Multiple Known ClustersCode0
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
Federated Asymptotics: a model to compare federated learning algorithms—0
Find Your Friends: Personalized Federated Learning with the Right Collaborators—0
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