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Training models with data from different contributors is an appealing approach, since when more and more data is used, the performance of the resulting models is usually better. A centralized solution requires that the data from the different users is gathered in a common location, something that is not always possible due to privacy/confidentiality restrictions. The MUSKETEER platform aims at solving Machine Learning (ML) problems using data from different contributors while preserving the privacy/confidentiality of the data and/or the resulting models. Essentially, it aims at deploying a distributed ML setup (Fig. 1) such that a model equivalent to the one…

Federated Machine Learning in Action: An Efficiency Assessment

Image by Barbara A Lane from Pixabay

Robots learn from each other

COMAU is an automation provider and its robots are installed in dozens of plants, in the automotive domain. In this context, these industrial automation customers are not eager to share their data.

Nevertheless, those data are precious for the robot maker with regards to both business (new added value services for customers) and technical aspects (to improve robot performance and quality).

Robot joints contain a belt that naturally loses its elasticity over time. With time and usage, the belt tensioning changes and actually, in order to prevent failures caused by a wrong tension, operators have to regularly check the belt…

Image by Barbara A Lane from Pixabay

How to properly train your Machine Learning model? That’s definitely not an easy question and it depends on many different aspects. As it will take too much time to make a complete analysis of all of them, we will concentrate only on one of them: the amount of training data.The quality of a Machine Learning model depends on the volume of training data used during the training process. Small amount of data can produce low accuracy models that cannot be really usable. In this case, we can consider two options to solve the problem: (i) produce more training data by…

Image by Barbara A Lane from Pixabay

Data, it’s always about the data!

In recent years, the advancements in Big Data technologies have fostered the penetration of AI and machine learning in many application domains, producing a disruptive change in the society and in the way many systems and services are organized. Machine learning technologies provide significant advantages to improve the efficiency, automation, functionality and usability of many services and applications. In some cases, machine learning algorithms are even capable of outperforming humans. …


MUSKETEER is an H2020 project developing an industrial data platform enabling privacy-preserving data sharing

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