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.

Key takeaways

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.

Flower: A Friendly Federated Learning Framework

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