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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 241–250 of 311 papers

TitleStatusHype
Personalized Federated Learning via ADMM with Moreau Envelope—0
Personalized Federated Learning via Amortized Bayesian Meta-Learning—0
Personalized Federated Learning via Backbone Self-Distillation—0
Personalized Federated Learning via Convex Clustering—0
Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion—0
Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization—0
Personalized Federated Learning via Learning Dynamic Graphs—0
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach—0
Personalized Federated Learning with Contextualized Generalization—0
Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application—0
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