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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 121–130 of 311 papers

TitleStatusHype
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
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning—0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification—0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
Agnostic Personalized Federated Learning with Kernel Factorization—0
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
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