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Cake for
Federated Learning

Train AI models across distributed datasets without centralizing sensitive data. Cake provides a modular, open-source stack for orchestrating federated learning across edge, partner, or multi-tenant environments.

 

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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.

Example use cases

Teams use Cake’s federated learning stack to collaborate across silos while protecting sensitive data:

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Cross-hospital model training

Enable hospitals to collaboratively train diagnostic models without sharing patient records.

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Multi-tenant SaaS analytics

Train personalization models per client without extracting or co-mingling datasets.

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IoT intelligence

Train models directly on edge devices to improve performance and privacy without massive data transfer.

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Pharmaceutical research across global trial sites

Enable drug companies to train models on trial data from multiple countries or institutions—without moving sensitive patient records across borders.

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Cross-branch fraud detection in financial institutions

Allow regional banks or subsidiaries to contribute to fraud models without centralizing sensitive customer transaction data.

Faster Time to Production

Personalized experiences on edge devices

Train models locally on user devices (e.g., phones, wearables) to support smart features like predictive text or health tracking—without uploading personal data to the cloud.

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"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

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Scott Stafford
Chief Enterprise Architect at Ping

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"With Cake we are conservatively saving at least half a million dollars purely on headcount."

CEO
InsureTech Company

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"Cake powers our complex, highly scaled AI infrastructure. Their platform accelerates our model development and deployment both on-prem and in the cloud"

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Felix Baldauf-Lenschen
CEO and Founder

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