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Flower: A Friendly Federated Learning Framework
Federated learning framework Flour enables easy experimentation and evaluation of algorithms, with features including scalability, configurability, carbon modeling, and comparison to data centers.
- Federated learning framework: Flour is an open-source framework that enables easy experimentation and evaluation of federated learning algorithms.
- Scalability: Flour allows for quick scaling to a large number of clients and devices, making it easy to run large-scale federated learning experiments.
- Configurability: Flour can be easily configured to run on different devices, networks, and computing architectures, allowing for realistic evaluation of federated learning algorithms.
- Carbon footprint: Flour includes a carbon modeling component that allows for evaluation of the carbon footprint of federated learning algorithms.
- Comparing federated learning to data centers: Flour enables comparison of federated learning to data centers in terms of carbon footprint and other system-oriented factors.
- Flour components: The framework consists of client support, a client SDK, a data center simulator, and a carbon modeling component.
- System-oriented research: Flour enables researchers to conduct system-oriented research in federated learning, including evaluation of different types of devices, networks, and computing architectures.
- Evaluation of federated learning algorithms: Flour allows for evaluation of federated learning algorithms using realistic system-oriented factors, including carbon footprint.
- Easy to use: Flour is designed to be easy to use and configure, making it accessible to researchers and developers without extensive expertise in federated learning.