Scaleout Federated Platform
A platform enabling powerful ML alliances.
AI faces two major challenges. One is that in most industries data exists in isolation. The other is the strengthening of data privacy and security.

We are developing a new distributed and secure architecture to solve these challenges. It will enable commercial federated machine learning alliances with full respect of the confidentiality of the data providers and the property rights of the companies that propose the machine learning models.
Federated Machine Learning
Digital systems in E-health, electric grids, smart homes, autonomous vehicles, personal robots, smart manufacturing, predictive maintenance - the list can be made long - all face the common challenge to balance the need of continuously learning systems that use sensitive, private data with the integrity of those interacting with the technology. Future AI-enabled solutions in all these areas will be based on advanced machine learning (ML).

The conventional way of doing ML is to pool all the data into one central place and use cluster computing to train predictive models. However, in many situations it is not possible to pool data. Recently, federated machine learning (FedML) technology has emerged as an alternative to centralized machine learning. FedML is a distributed machine learning approach which enables training on decentralised data. A server coordinates a network of nodes, each of which has local, private training data. The nodes contribute to the construction of a global model by training on local data , and the server combines non-sensitive node model contributions into the global model. This enables advanced ML also when data is private, sensitive or expensive to move (e.g. data-intensive infrastructure at the edge). In this sense it is a paradigm shift in the machine learning world.
Main issues with centralized machine learning
Generally speaking, more data means better machine learning models, but in many instances, data cannot be gathered in a central location.

Reasons for this include:
  • Private/Proprietary data — Sharing valuable business data with someone else is not an option.
  • Regulated data — GDPR, HIPAA, etc.
  • Practical blockers — data is too big, the network connection is expensive, slow or unreliable.
Benefits of FedML
Federated learning addresses the fundamental problems of centralized AI such as privacy, ownership, and locality of data. It extends, even disrupts, the centralized AI paradigm in which better algorithms always comes at the cost of collecting more and more sensitive data.

Federated learning enables:
  • Data security and privacy where data never moves
  • Reduced communication complexity and costs
  • Powerful data network effects in industries where data cannot be transferred
Factors influencing the FedML strategy
The best strategy to construct a global model depends on a number of factors including:
  • What can be shared? (training data, test/validation data, model parameters or predictions only)
  • The problem and type of model (CNN, SVM, Random Forest etc)
  • The local training cost and network communication cost
  • The target size of the data alliance
  • The need for valid prediction intervals or set predictions
  • Is the target prediction endpoints in embedded systems or in SaaS?

For this reason we aim for our solutions to be flexible and ML framework agnostic supporting sustainable development of AI.
We believe that federated machine learning holds exceptional promise. The reason is that it has equal potential for constructing integrity-preserving machine learning models at scale, and for enabling powerful business models centred around data-alliances.

Our current priority at Scaleout is to build our Federated platform to bridge the gap between academic research on privacy-preserving learning on the one hand, and the requirements of production grade systems for large scale applications on the other hand. Our vision is a machine learning framework-agnostic platform that removes the technical, security and trust-related barriers to forming an ML-alliance, and that lets data scientists focus on building federated models.

Learn more about our project for management of business logic of setting up alliances, the governance of the alliances, the constructions and models, and the life cycle management of alliances. With support from Vinnova, Sweden's innovation agency.

Get in touch if you want to learn more about privacy-preserving federated machine learning (FedML). And make sure to visit our Lab section where we explore different aspects of privacy-preserving ML and decentralisation.
Phone: +46(0)18-7770303

We are located at Epicenter in Stockholm and Juvelen in Uppsala.