Flower provides a friendly, open-source framework for building and deploying federated learning systems. It allows users to easily integrate federated capabilities with existing machine learning projects using popular frameworks like PyTorch and TensorFlow. The platform supports enterprise-grade deployment and research into decentralized foundation model training.
Funding
$23.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
BFounders
Product
Problem
Developing and deploying federated learning models requires specialized infrastructure, coordination across heterogeneous devices, and expertise that many organizations lack, limiting the adoption of privacy‑preserving AI at scale.
Solution
Flower provides an open‑source framework and a cloud‑based platform that simplify the creation, simulation, and production deployment of federated learning applications. The platform includes a community marketplace where developers can share ready‑made federated AI apps, tutorials, and expert services, accelerating projects from prototype to production. Flower’s infrastructure supports secure cross‑device model training across any industry, offering both self‑service tools and optional expert assistance to handle scaling, compliance, and operational concerns. Users can manage federations through a web UI, control access with role‑based permissions, and integrate private or public federations as needed.
Target Audience
Primary users are AI researchers, data scientists, and machine learning engineers who need to implement privacy‑preserving federated learning, as well as enterprises seeking to deploy federated AI across distributed devices.
Features
- Open‑source federated learning SDKs for Python, iOS, Android, and C++ to build clients on diverse devices
- Flower Hub marketplace for publishing and discovering federated AI apps, tutorials, and services
- Web UI for creating, monitoring, and managing unlimited federations with audit logs and RBAC
- Support for both public and private federations, including confidential compute and verified apps on higher tiers
- Scalable SuperGrid backend offering monthly credit packages and enterprise‑grade features such as SOC2 compliance
- Optional Flower Labs services ranging from on‑demand technical support to full end‑to‑end implementation and operation