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THE CAKE DIFFERENCE
From centralized data risks to
decentralized intelligence
Centralized training
High risk, high friction: Moving data into one place for training creates privacy concerns and logistical headaches.
- Requires transferring raw data across systems or orgs
- High compliance and regulatory risk in sensitive domains
- Difficult to scale across partners, hospitals, or edge locations
- No native support for collaboration without exposing data
Federated learning with Cake
Train smarter across distributed environments: Cake lets you build privacy-preserving learning pipelines with full control and observability.
- Train on distributed nodes without moving raw data
- Built-in support for model aggregation, drift detection, and evals
- Supports healthcare, finance, and edge deployment scenarios
- Secure, auditable, and compatible with open FL frameworks like Flower and FedML
EXAMPLE USE CASES
Teams use Cake’s federated learning stack to
collaborate across silos while protecting
sensitive data
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.
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.
Cross-branch fraud detection in financial institutions
Allow regional banks or subsidiaries to contribute to fraud models without centralizing sensitive customer transaction data.
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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Scott Stafford
Chief Enterprise Architect at Ping
CEO
InsureTech Company
Felix Baldauf-Lenschen
CEO and Founder
COMPONENTS
Tools that power Cake's
federated learning stack
Ray Tune
Ray Tune is a Python library for distributed hyperparameter optimization, built on Ray’s scalable compute framework. With Cake, you can run Ray Tune experiments across any cloud or hybrid environment while automating orchestration, tracking results, and optimizing resource usage with minimal setup.
MLflow
Track ML experiments and manage your model registry at scale with Cake’s automated MLflow setup and integration.
Slurm
Slurm is an open-source job scheduler for high-performance computing environments. Cake integrates Slurm-managed compute clusters into AI workflows, automating resource allocation, scaling, and observability.
Kubeflow
Kubeflow is an open-source machine learning platform built on Kubernetes. Cake operationalizes Kubeflow deployments, automating model training, tuning, and serving while adding governance and observability.
PyTorch
PyTorch is a widely used open-source machine learning framework known for its flexibility, dynamic computation, and deep learning support.
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