Federated learning, private by design

Unleash the potential of Data

With Acuratio's Multicloud Federated Learning Platform organizations around the world are able to unlock the value of data by combining datasets without compromising privacy.

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Breaking Down Silos

Many companies anticipate huge benefits from machine learning, but cannot access the data they need due to privacy or regulatory concerns. Acuratio's platform allows companies to leverage and combine different types of data to develop more accurate models or compute aggregate statistics, while preserving both model and data privacy.

Horizontal Learning

Train a ML Learning model with distributed examples of the same data with Split Learning or Federated Averaging. Example: Data localization, fraud, risk, forecasting…

Vertical Learning

Combine two or more data sources with a common set of users but different features with Split Learning and Private Set intersection. Example: Pricing, retention…

Private Analytics

Keep individual information private while allowing organizations to compute aggregate statistics or segment your audience, Private Join & Compute. Example: conversion, cohorts...

Multicloud Federated Learning

Utility and privacy without tradeoffs, get insights or improve your machine learning models by incorporating new sources of data, without sacrificing model or data privacy.

Multicloud
On Premise
Ease of use
Data Privacy
Model Privacy

Ease of Use

From data to production, create project, connect data, train model, test model.

1

Install the platform

Connect your data lake. On the cloud or on-prem.
2

Train adding a few lines of code

Deploy your current TensorFlow models or compute aggregated statistics.
3

Unlock your data!

Share, monetize or get insights from your data.

Features

The most complete Federated Learning platform. Privately train, deploy, serve, and manage Machine Learning Models.

	Teams Edition	Enterprise Edition
Vertical Federated Learning Different configurations that suit your needs.
Horizontal Federated Learning Federated Averaging and Split Learning for TensorFlow or Keras.
Differential Privacy Seamless integration during training.
Secure Aggregation Protocol For cryptographically private Federated Learning.
Role Based Authentication Granular access to data, models and results.
Focused Updates Sparse ternary compression and Random Rotation Matrices.
GPU Support Training and serving performance and efficiency.
Collaboration Role Based Access Control and Team Management.
Audit Logs Monitor access to your data.
Multicloud On-premise, cloud and hybrid deployments.
Model Serving Seamless deployment of ML models.
Personalized Support Get quick responses and solutions from our experts.