Towards a sustainable Industry 4.0
Privacy-preserving machine learning technology is critical
for a sustainable development of AI for Industry 4.0
We should not have to choose between enjoying the productivity increase enabled by advanced machine learning, and our personal integrity. Unfortunately, the combination of a decade of big data analytics dominated by naive optimism, the emergence of cloud computing with its convenient scaling of infrastructure, and weak regulatory frameworks, has driven both business models and technology to focus almost exclusively on enabling the collection and analysis of massive centralized data sets. Today, machine learning technology over centralized data has matured to the point that graphical point-and-click systems for deep learning make model building possible also for non-data scientist.
There are few, if any, production-grade alternatives that challenge the centralized machine learning paradigm. However, there is an increasing voice in the global research community speaking up for the development of privacy-aware machine learning technologies. Of the various approaches to privacy-preserving machine learning, we believe that those that enable modelling on decentralized data assets, such as federated machine learning, hold the most promise to balance the need for data-privacy and practical usefulness at scale.

Scaleout's mission is to contribute to this development by bridging the gap between academic research and the requirements of production-grade applications of federated machine learning.
Industry 4.0 powered by AI based on advanced machine learning will lead to large gains for industry and society, but data-private machine learning systems are critical to preserve the integrity of those operating and interacting with edge and fog devices. Similarly, the massive amount of data generated at the edge calls for decentralized ML schemes. Federated machine learning is a technology that holds great promise to address these needs.
A safer and less privacy-invasive Industry 4.0
Digitalization of the healthcare sector, smart homes, autonomous vehicles, personal robots, smart manufacturing, predictive maintenance - the list can be made long - all face a common challenge to balance the massive improvement in productivity enabled by continuously learning systems with the privacy of the people using and interacting with the technology.

Federated ML offers the possibility of continuous improvement of models using active learning without sharing sensitive data, and without needing to remove/deanonymize valuable data records.
Democratization of AI
In today's data economy, actors with a long-term strategy and capacity to collect massive amounts of data have a very large, unfair advantage when it comes to constructing strong machine learning models. The consequence might be a rapid shift in power to a relatively small number of actors to set the stage for our future AI society.

A highly scalable federated machine learning technology has the potential to challenge this development by ensuring that many smaller entities (even individual citizens) can team up to form data and machine learning alliances as powerful as even the largest single actor.
FedML technologies open up for new business models centered on 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 a ML-alliance, and that lets data scientists focus on building federated models without being bogged down by the challenges outlined above.
Do you want to learn more?
Find out about the Scaleout Federated Learning platform here. Or get in touch below and let's start a discussion!
Phone:
+46 (0) 70-880 15 70

Mail:
contact@scaleoutsystems.com


By clicking the button you agree with our Privacy Policy.