About Flower Labs

Flower is a federated learning platform that enables the integration of machine learning workloads across various frameworks and programming languages. It facilitates secure data collaboration among distributed clients, allowing organizations to leverage decentralized data for improved model training and evaluation without compromising privacy.

```xml <problem> Traditional machine learning approaches require centralized datasets, which raises privacy concerns and limits the ability to leverage data from distributed sources. Existing federated learning solutions often lack flexibility in terms of supported frameworks and deployment environments. </problem> <solution> Flower is a federated learning framework designed to unify machine learning, analytics, and evaluation across diverse workloads, ML frameworks, and programming languages. It enables secure collaboration on decentralized data, allowing organizations to train and evaluate models without compromising data privacy. The platform supports a wide range of frameworks, including PyTorch, TensorFlow, and Hugging Face, and can be deployed across cloud, mobile, and edge environments. Flower facilitates the transition from research to production by providing a scalable and user-friendly infrastructure for federated learning projects. </solution> <features> - Framework-agnostic design supporting PyTorch, TensorFlow, Hugging Face, and more - Scalable architecture capable of handling tens of millions of clients - Compatibility with cloud, mobile, and edge deployment environments (AWS, GCP, Azure, Android, iOS, Raspberry Pi, Nvidia Jetson) - Support for federated learning, analytics, and evaluation - Easy-to-use Python API requiring as little as 20 lines of code for a full federated learning system - Platform independence, ensuring interoperability across different operating systems and hardware platforms </features> <target_audience> The primary audience includes machine learning researchers and practitioners, data scientists, and organizations seeking to leverage federated learning for privacy-preserving model training and evaluation across distributed datasets. </target_audience> ```

What does Flower Labs do?

When was Flower Labs founded?

Flower Labs was founded in 2023.

How much funding has Flower Labs raised?

Flower Labs has raised 23600000.

Founded

2023

Funding

23600000

Major Investors

Y Combinator, Betaworks, Felicis, Pioneer Fund, First Spark Ventures

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Flower Labs

AI-Generated Overview – may contain errors

Executive Summary

flower.ai LinkedIn Profile

Founded 2023

Funding

Estimated Funding

$20M+

Major Investors

Y Combinator, Betaworks, Felicis, Pioneer Fund, First Spark Ventures

Team

No team information available.

Company Description

Problem

Traditional machine learning approaches require centralized datasets, which raises privacy concerns and limits the ability to leverage data from distributed sources. Existing federated learning solutions often lack flexibility in terms of supported frameworks and deployment environments.

Solution

Flower is a federated learning framework designed to unify machine learning, analytics, and evaluation across diverse workloads, ML frameworks, and programming languages. It enables secure collaboration on decentralized data, allowing organizations to train and evaluate models without compromising data privacy. The platform supports a wide range of frameworks, including PyTorch, TensorFlow, and Hugging Face, and can be deployed across cloud, mobile, and edge environments. Flower facilitates the transition from research to production by providing a scalable and user-friendly infrastructure for federated learning projects.

Features

Framework-agnostic design supporting PyTorch, TensorFlow, Hugging Face, and more

Scalable architecture capable of handling tens of millions of clients

Compatibility with cloud, mobile, and edge deployment environments (AWS, GCP, Azure, Android, iOS, Raspberry Pi, Nvidia Jetson)

Support for federated learning, analytics, and evaluation

Easy-to-use Python API requiring as little as 20 lines of code for a full federated learning system

Platform independence, ensuring interoperability across different operating systems and hardware platforms

Target Audience

The primary audience includes machine learning researchers and practitioners, data scientists, and organizations seeking to leverage federated learning for privacy-preserving model training and evaluation across distributed datasets.