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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 151–175 of 311 papers

TitleStatusHype
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives—0
PFedDST: Personalized Federated Learning with Decentralized Selection Training—0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing—0
pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning—0
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning—0
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning—0
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization—0
pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data—0
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning—0
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation—0
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning—0
IP-FL: Incentivized and Personalized Federated Learning—0
pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup—0
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population—0
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
SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning—0
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