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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 31–40 of 311 papers

TitleStatusHype
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
Characterizing Internal Evasion Attacks in Federated LearningCode1
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data DistributionCode1
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI SynthesisCode1
An Empirical Study of Personalized Federated LearningCode1
Personalized Federated Learning via Variational Bayesian InferenceCode1
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT ImagingCode1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge IntelligenceCode1
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