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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 91–100 of 311 papers

TitleStatusHype
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
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning—0
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series DataCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
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