Share this post
Real-world federated AI deployments need to satisfy a long list of security, compliance and operational requirements such as strict data protection, infrastructure scalability, high workload throughput, multi-tenant operations, and the ability to train the next generation of foundational models, to name a few. They must also satisfy governance, audit, and compliance constraints that vary across regulated domains. These conditions are typically not optional and they determine whether an Federated AI platform can be deployed at all.
In this blogpost, we focus on a set of Flower architectural patterns and capabilities — strengthened further by our recently announced SuperGrid — that directly address common enterprise requirements. Each section highlights a requirement, explain why it matters, and shows the specific Flower mechanisms that meet it.
Pure Pull Architecture for Regulated Data Environments
In many regulated domains, such as healthcare, life sciences, and finance, infrastructures that hold sensitive data, like Secure Data Environments, impose strict networking rules. Typically, inbound traffic pushed from external servers are forbidden because it introduces uncontrolled data flows and enlarges the attack surface.
Flower operates solely through client-initiated communication. When running Flower with the Deployment Engine, clients (SuperNodes) request tasks from the server (SuperLink) and return computation results. All communication is initiated within the protected environment and flows outward, aligning precisely with firewall, compliance, and security controls that forbid inbound orchestration traffic. This makes Flower particularly suited for federated AI across secure enclaves or regulated data environments, where traditional push-based architectures are not an acceptable option.
Isolation and Scalable Workload Execution
Enterprises require strict process isolation and ability to scale workloads on demand, avoiding dependency conflicts or shared execution environments across different tenants or projects.
Flower addresses this with the Flower Isolation Model, with SuperLink and SuperNodes running in Process Mode. With this, the infrastructure components, i.e. SuperLink and SuperNodes, are kept separate from the execution components, which are the ServerApp and ClientApp processes. When a run executes, ServerApp and ClientApps are launched via a dedicated isolated process (SuperExec). This separation ensures that federated workloads do not share dependencies with orchestration logic, eliminating risk of shared-dependency vulnerabilities or unintended side-effects.
Using our official, hardened and security-scanned container images hosted on DockerHub for SuperLink and SuperNodes further reduces attack surface, containing only the minimal set of dependencies required to operate Flower. Scaling is straightforward since containers can be spun up or down without impacting others.
The Flower architecture matches enterprise requirements for isolation, separation of concerns, and scalable, on-demand compute. The Flower Isolation Model isolates components on four levels: the process level, the container level, the instance level and the network level.
Native Support for Foundational-Scale Models
Enterprise R&D groups increasingly train transformer-scale or domain-specific foundational models. These models often contain hundreds of billions of parameters, and the ecosystems developing them —such as the BloodCounts! consortium or Eye2Gene— require support for both larger models and larger datasets across multiple institutions. Under such settings, transporting model weights becomes a bottleneck when the infrastructure cannot handle large payloads efficiently.
Flower solves this through it's unique Content-Addressable Message stack. Part of this stack is automatic message chunking, which streams arbitrarily large models without protocol-level limits. With this mechanism, Flower can transfer and train trillion-parameter models. In addition, Flower's Content-Addressable Messaging stack enables deduplication of transmitted chunks. This dramatically reduces network usage and improves efficiency, making large-scale foundational model training feasible across distributed environments.
An additional benefit of this message stack is fully verifiable and compliant audit logging: with Content-Addressable Messages, you get audit logs that trace the execution of each run down to each single gradient.
Multi-Federation Support Without Infrastructure Duplication
Enterprises typically run many workloads at once with multiple users. They need federation isolation, access permissions and policies for users and projects, but do not want to deploy new infrastructure for every use case given that it increases the infrastructure overhead and cost.
With the release of Flower 1.24.0, Flower SuperGrid resolves this by allowing a single SuperNode to participate in more than one federation, as well as allowing a user to participate in more than one federation. Users can run distinct workloads concurrently, each isolated at the federation level, without multiplying deployments. This enables clean and compliant multi-tenant operation while keeping the operational footprint manageable, ensuring that infrastructure teams do not duplicate effort for each additional workload.
In Preview: Confidential Compute for Data-in-Use Protection
Enterprises increasingly require data-in-use protection, especially in regulated domains. Confidential compute is the emerging requirement: hardware-backed attestation and protected execution environments for aggregation logic. Confidential computing, which is available on select Intel, AMD, and Nvidia hardware, keeps sensitive applications and workloads inside of an enclave, a region of memory that cannot be accessed even by privileged system users.
With confidential computing on SuperGrid, untrusted parties can be kept outside the trust boundary, and workload correctness can be cryptographically verified through application-level attestation. These capabilities, which will soon be made generally available through SuperGrid, even grant competitors or nation states the opportunity to collaborate on mutually beneficial problems for the first time.
Next Steps
This blogpost highlights several key enterprise requirements, ranging from dependency isolation, networking, to multi-federations and data-in-use-protections, which are necessary for production platforms. The Flower Framework and Flower SuperGrid provide the architectural patterns, operational controls, and scale necessary for such production platforms across regulated environments.
If your organization is looking for a unified, scaleable, and secure federated AI platform, Flower provides a complete and ready solution today.
To learn more about how to use Flower for your deployments, check out our documentation here. To sign up for SuperGrid early access, join the waitlist here.
Share this post