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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 111–120 of 311 papers

TitleStatusHype
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
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