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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 111–120 of 311 papers

TitleStatusHype
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
PeFLL: Personalized Federated Learning by Learning to LearnCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
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