Flower Team Releases Flower 1.0: A Friendly Federated Learning Framework

Developers of artificial intelligence (AI) and data scientists frequently combine a wide range of expertise from fields like mathematics and statistics, machine learning, AI, databases, cloud computing, and data visualization. 

Researchers and practitioners have created new tools to increase the effectiveness of data science work in data science and AI by allowing various domain experts to collaborate. The data science community frequently uses Jupyter Notebook, JupyterLab, and Google Colab as significant coding environments. Developers can write code and narratives (such as descriptions of data, code, or visualizations) in cells next to each other in each environment.

The Flower Team has recently released Flower 1.0 stable, a welcoming framework for cooperative AI and data research. It opens up a wide range of researchers and engineers to novel methodologies, including federated learning, federated evaluation, federated analytics, and fleet learning.

The team gives significant recognition to the Flower Community, helping them reach this milestone. As the team states, they had numerous discussions with users, partners, researchers, and business professionals for the creation of Flower 1.0. The Flower has gained access to various sectors in the medical, financial, automotive, pharmaceutical, industrial, and mobile device industries.

Major new features and numerous tiny API cleanups are included in Flower 1.0, but the focus on Python API stability is the most significant change. Other features of Flower 1.0 are as follows:

1. The Virtual Client Engine allows unique Server implementations and is reliable.
2. There are no required Client/NumPyClient methods.
3. A configuration dictionary is supported by get_parameters.
4. Many small API improvements lead to a more consistent developer environment.
5. More information on these and other changes may be found in the Flower 1.0 changelog.

As a great initiative, the team provides involving Flower’s use, introduction to federated learning, and using strategies in federated learning. The team open-sourced their code and baselines to allow the research community to accelerate their development and improve upon this work. 

References:

• https://flower.dev/blog/2022-07-28-announcing-flower-1.0
• https://github.com/adap/flower