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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

Distributed AI is a computing paradigm that bypasses the need to move vast amounts of data and provides the ability to analyze data at the source. Distributed AI APIs built by IBM Research is a set of RESTful web services with data and AI algorithms to support AI applications across hybrid cloud, distributed, and edge computing environments. Each Distributed AI API addresses the challenges in enabling AI in distributed and edge environments with APIs. The Distributed AI APIs do not focus on the basic requirements of creating and deploying AI pipelines, for example, model training and model serving. You would use your favorite open-source packages such as TensorFlow or PyTorch. Then, you can containerize your application, including the AI pipeline, and deploy these containers at the distributed locations. In many cases, it’s useful to use a container orchestrator such as Kubernetes or OpenShift operators to automate the deployment process.

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

Developers interested in a solution offering data and AI algorithms to support their AI applications

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

No information available.
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

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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

IBM
United States
developer.ibm.com/apis/catalog/edgeai--distributed-ai-apis/Introduction/

Alternatives

Alternatives

Keepsake

Keepsake

Replicate
AWS Neuron

AWS Neuron

Amazon Web Services
DeepSpeed

DeepSpeed

Microsoft

Categories

Categories

Integrations

PyTorch
TensorFlow
Amazon Web Services (AWS)
Apple iOS
Docker
Google Cloud Platform
Hugging Face
JAX
Keras
Kubernetes
MXNet
Microsoft Azure
Modern Leadership (MLX)
NVIDIA Jetson
NumPy
Python
Raspberry Pi OS
Red Hat OpenShift
pandas
scikit-learn

Integrations

PyTorch
TensorFlow
Amazon Web Services (AWS)
Apple iOS
Docker
Google Cloud Platform
Hugging Face
JAX
Keras
Kubernetes
MXNet
Microsoft Azure
Modern Leadership (MLX)
NVIDIA Jetson
NumPy
Python
Raspberry Pi OS
Red Hat OpenShift
pandas
scikit-learn
Claim Flower and update features and information
Claim Flower and update features and information
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Claim IBM Distributed AI APIs and update features and information