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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 31–40 of 311 papers

TitleStatusHype
SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
Integrating Personalized Federated Learning with Control Systems for Enhanced Performance—0
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning—0
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching—0
pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup—0
pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data—0
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning—0
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression RecognitionCode0
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