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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 231–240 of 311 papers

TitleStatusHype
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning—0
Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos—0
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data—0
Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matching—0
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