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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 161–170 of 311 papers

TitleStatusHype
Collaborative Chinese Text Recognition with Personalized Federated Learning—0
Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity—0
Prompt-based Personalized Federated Learning for Medical Visual Question Answering—0
Prototype-Based Layered Federated Cross-Modal Hashing—0
RCC-PFL: Robust Client Clustering under Noisy Labels in Personalized Federated Learning—0
Reliable and Interpretable Personalized Federated Learning—0
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation—0
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks—0
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach—0
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning—0
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