Infrastructure & MLOps

Flower

Flower is a federated AI framework designed to support federated learning, analytics, and evaluation across diverse workloads. It provides a unified approach that allows users to federate any machine learning workload regardless of the ML framework or programming language used. This flexibility enables integration with a wide range of AI development environments and use cases. Flower aims to facilitate collaboration and distributed model training by abstracting the complexities involved in federated learning setups.

Updated Jan 4, 2026unknown

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Overview

A unified federated AI framework supporting any workload, ML framework, and programming language.

Pricing

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Distributed Model Training

Training machine learning models across multiple decentralized devices while keeping data local.

Cross-Framework Federated Learning

Integrating models developed in different ML frameworks into a single federated learning workflow.

Quick Start

Set Up Federated Environment

Prepare the distributed devices or nodes where the federated learning will take place.

Integrate ML Workloads

Implement your machine learning models in your preferred framework and programming language, then integrate them with Flower.

Run Federated Training

Execute the federated learning process using Flower's framework to coordinate training across nodes.

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