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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 51–60 of 311 papers

TitleStatusHype
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant ClientsCode0
Personalized Federated Learning via Feature Distribution AdaptationCode1
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-TuningCode0
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning—0
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering—0
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer—0
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic—0
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax GuaranteesCode0
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