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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 201–210 of 311 papers

TitleStatusHype
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration—0
Advances and Challenges in Meta-Learning: A Technical Review—0
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
Agnostic Personalized Federated Learning with Kernel Factorization—0
An Optimal Transport Approach to Personalized Federated Learning—0
A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss—0
A novel parameter decoupling approach of personalized federated learning for image analysis—0
A Parameter Aggregation Strategy on Personalized Federated Learning—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
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