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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 81–90 of 311 papers

TitleStatusHype
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
Asynchronous Personalized Federated Learning through Global Memorization—0
Decentralized Personalized Federated Learning—0
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
Advances and Challenges in Meta-Learning: A Technical Review—0
FedMCSA: Personalized Federated Learning via Model Components Self-Attention—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
Decentralized Directed Collaboration for Personalized Federated Learning—0
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