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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 201–210 of 311 papers

TitleStatusHype
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning—0
Lower Bounds and Optimal Algorithms for Personalized Federated Learning—0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning—0
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning—0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning—0
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM—0
How to Backdoor HyperNetwork in Personalized Federated Learning?—0
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics—0
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