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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 101–125 of 311 papers

TitleStatusHype
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data—0
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated LearningCode0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification—0
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser SchemeCode0
Personalized Federated Learning via StackingCode0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach—0
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-TuningCode1
Client-supervised Federated Learning: Towards One-model-for-all Personalization—0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter UpdateCode0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning—0
RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection—0
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune—0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection—0
FedD2S: Personalized Data-Free Federated Knowledge Distillation—0
Prompt-based Personalized Federated Learning for Medical Visual Question Answering—0
Personalized Federated Learning for Statistical Heterogeneity—0
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
Spectral Co-Distillation for Personalized Federated LearningCode0
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation—0
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