adversarial-16
Federated Learning
3260 papers • 126 benchmarks • 313 datasets
Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model. This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in federated-learning-33
Trend
Dataset
Best Model
Actions
Cityscapes heterogeneous
Cityscapes heterogeneous
Landmarks-User-160k
Landmarks-User-160k
Libraries
iUse these libraries to find federated-learning-33 models and implementations
15 papers 311
Datasets
No datasets available.
