Cake for
Federated Learning

Overview

In healthcare, finance, and multi-tenant SaaS, the data you need to train great models is often fragmented or protected by regulation. Federated learning solves this by training models where the data lives, without ever sharing raw inputs. But orchestrating federated workflows is complex without the right infrastructure.

Cake provides a modular, cloud-agnostic platform that simplifies federated learning at scale. Use open-source libraries like Flower or FedML to coordinate model updates, manage training jobs with Kubeflow Pipelines, and track performance centrally with MLflow and Evidently. You control the orchestration and observability, without compromising on security or compliance.

Because Cake is built from composable, open-source components, you can integrate the latest federated learning frameworks and adapt to evolving data-sharing agreements, while avoiding vendor lock-in and reducing infrastructure spend.

Key Benefits

• Train on distributed data: Run training across edge devices, partners, or tenants without centralizing raw data.
  
  
• Maintain compliance and privacy: Meet data residency and governance requirements across all regions and clients.
  
  
• Use open-source FL frameworks: Integrate Flower, FedML, or custom aggregation logic seamlessly.
  
  
• Deploy across clouds or hybrid setups: Coordinate learning across on-prem, cloud, or multi-region environments.
  
  
• Track and improve global performance: Aggregate metrics, evaluate drift, and optimize collaboration over time.