Flower

What is Federated Learning?

Federated Learning is a machine learning technique that enables training models on decentralized data sources while preserving data privacy. Instead of centralizing the data, models are trained locally on each device or data source, and only model updates are shared with a central server for aggregation.

What frameworks are supported by Flower?

Flower supports popular machine learning frameworks like TensorFlow, PyTorch, and Hugging Face, allowing developers to easily integrate federated learning into their existing projects.

How does Flower ensure data privacy?

Flower facilitates federated learning, where models are trained locally on decentralized data sources, and only model updates are shared with a central server. This approach minimizes the risk of exposing sensitive data.

What is Flower Simulation?

Flower Simulation provides a simulation environment for testing and evaluating federated learning strategies before deploying them in real-world scenarios. This helps optimize resource allocation and reduce deployment risks.