Dask vs. Flower vs. NumPy vs. python-sql Comparison Chart

Flower is an open source federated learning framework designed to simplify the development and deployment of machine learning models across decentralized data sources. It enables training on data located on devices or servers without transferring the data itself, thereby enhancing privacy and reducing bandwidth usage. Flower supports a wide range of machine learning frameworks, including PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and XGBoost, and is compatible with various platforms and cloud services like AWS, GCP, and Azure. It offers flexibility through customizable strategies and supports both horizontal and vertical federated learning scenarios. Flower's architecture allows for scalable experiments, with the capability to handle workloads involving tens of millions of clients. It also provides built-in support for privacy-preserving techniques like differential privacy and secure aggregation.

python-sql is a library to write SQL queries in a pythonic way. Simple selects, select with where condition. Select with join or select with multiple joins. Select with group_by and select with output name. Select with order_by, or select with sub-select. Select on other schema and insert query with default values. Insert query with values, and insert query with query. Update query with values. Update query with where condition. Update query with from the list. Delete query with where condition, and delete query with sub-query. Provides limit style, qmark style, and numeric style.