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About

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.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Machine learning practitioners and researchers in search of a tool to implement privacy-preserving, decentralized model training across diverse devices and platforms

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Flower
Founded: 2023
Germany
flower.ai/

Company Information

scikit-learn
United States
scikit-learn.org/stable/

Alternatives

Alternatives

Gensim

Gensim

Radim Řehůřek
Keepsake

Keepsake

Replicate
ML.NET

ML.NET

Microsoft
MLlib

MLlib

Apache Software Foundation
Keepsake

Keepsake

Replicate

Categories

Categories

Integrations

NumPy
Python
Amazon Web Services (AWS)
Android
Apple iOS
DagsHub
Docker
Flower
Hugging Face
JAX
Keras
MLJAR Studio
MXNet
Matplotlib
Microsoft Azure
ModelOp
Modern Leadership (MLX)
PyTorch
Raspberry Pi OS
scikit-learn

Integrations

NumPy
Python
Amazon Web Services (AWS)
Android
Apple iOS
DagsHub
Docker
Flower
Hugging Face
JAX
Keras
MLJAR Studio
MXNet
Matplotlib
Microsoft Azure
ModelOp
Modern Leadership (MLX)
PyTorch
Raspberry Pi OS
scikit-learn
Claim Flower and update features and information
Claim Flower and update features and information
Claim scikit-learn and update features and information
Claim scikit-learn and update features and information