Bringing Flower to Production: Duality Adds Native Support for the Popular Federated Learning Framework

As federated learning (FL) matures from academic research to real-world deployment, interoperability has become critical. Many organizations begin their FL journey using open-source frameworks like Flower (flwr) valued for its simplicity, flexibility, and large developer community but then face a challenge when trying to operationalize those workloads securely at scale.

Today, Duality is bridging that gap. We’re excited to announce native support for Flower within the Duality Platform, enabling organizations to bring their existing Flower-based federated learning code directly into a secure, governed, and production-ready environment.

From Research to Production Without Rewriting Code

Flower is a lightweight and flexible federated learning framework widely used in research, prototyping, and simulation environments. It provides a Pythonic interface for writing FL logic defining what runs on each client and how results are aggregated while leaving orchestration and infrastructure to the user.

By integrating Flower directly into the Duality Platform, developers can now run Flower workloads in production as-is, while benefiting from Duality’s enterprise-grade features, including:

• Governance and Policy Control: Define who participates, under what data policies, and which models could run on which data. .
• Privacy-Enhancing Technologies (PETs):Add an additional layer of protection on top of the federated workload by encrypting the intermediate weights and running the aggregation step inside a trusted execution environment. .
• Scalable Orchestration: Manage distributed training jobs across multiple data owners, with automated lifecycle management and monitoring.
• Compliance and Auditability: Enforce organizational and regulatory requirements without altering core federated logic.

In short if your team has written FL code in Flower, you can now run it securely within Duality, without rebuilding your stack.

Complementary Ecosystem: Flower and FLARE

The federated learning ecosystem spans both research and enterprise frameworks each serving a distinct purpose. Flower is a developer-friendly framework designed for flexibility and experimentation, while NVIDIA FLARE focuses on scalable orchestration and production deployment.

Duality’s platform now acts as a bridge between these worlds. Whether workloads originate in Flower, FLARE, or other open-source frameworks, Duality provides the unifying layer of governance, policy enforcement, and PET-based security required to bring federated learning to mission scale.

Why It Matters

This new integration means organizations no longer have to choose between open-source agility and enterprise compliance. Researchers can develop FL models using Flower’s familiar API, then move seamlessly into a secure Duality environment for production, maintaining continuity of code and trust in governance.

The result is a faster path from concept to deployment: less friction, less duplication, and full confidence that sensitive data never leaves protected boundaries.

Looking Ahead

Duality’s support for Flower marks another milestone in our mission to make secure collaborative AI accessible, flexible, and interoperable.

As we continue deepening integrations across the federated learning ecosystem including NVIDIA FLARE and other PET-compatible frameworks Duality is creating a single, governed platform where innovation and compliance can truly coexist.

If your team has existing Flower-based FL workloads or prototypes and is ready to scale securely, we’d love to explore how Duality can help.