methodology-17
Personalized Federated Learning
3260 papers • 126 benchmarks • 313 datasets
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.
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Benchmarks
These leaderboards are used to track progress in personalized-federated-learning-34
Trend
Dataset
Best Model
Actions
CIFAR-10
CIFAR-10
CIFAR-100
CIFAR-100
Omniglot
Omniglot
Libraries
iUse these libraries to find personalized-federated-learning-34 models and implementations
4 papers 1,150
Datasets
No datasets available.
Subtasks
No subtasks available.
