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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

Showing 6170 of 311 papers

TitleStatusHype
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated LearningCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical RepresentationsCode0
Learn What You Need in Personalized Federated LearningCode0
Flow: Per-Instance Personalized Federated Learning Through Dynamic RoutingCode0
Adaptive Expert Models for Personalization in Federated LearningCode0
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal HealthcareCode0
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