FEDERATED LEARNING: GET TO KNOW PRIVACY-PRESERVING DATA ANALYSIS

WHAT IS FEDERATED LEARNING?

Federated Learning is a decentralised and privacy-friendly form of machine learning. This means that there is no need for a central database to hold all of the sensitive data, so these data cannot be leaked. Instead of bringing the data to the machine learning model, Federated Learning brings the machine learning model to the data.

In this way, the training of the models is broken down into sub-calculations that are performed locally at an organisation. After carrying out the calculations, only the anonymised (intermediate) results are shared with the organisations conducting the research, not the privacy-sensitive data itself.

WHICH PROBLEMS DOES FEDERATED LEARNING SOLVE?

Federated Learning solves two major problems of data analysis: improved qualitative analyses for society and safeguarding of citizens’ rights in relation to privacy. 

These days, analysing large amounts of data is easier than ever before. Computing power is increasing and algorithms are becoming ever more advanced. Although more and more data is available for valuable analyses, there are an increasing number of societal objections to the use of sensitive data.

WHAT CAN ORGANISATIONS GAIN FROM FEDERATED LEARNING?

Federated Learning allows you to harness data without violating privacy. The amount of available data is greater as you can analyse data from multiple databases. In turn, the results of a study are more reliable. This means better predictions and better models, leading to much more informed (policy) decisions. 

An example? Take cancer research. With Federated Learning, you can analyse anonymised data on things like successful treatment methods for each type of cancer in different people across all hospitals without violating patient privacy.

WHAT DOES TNO DO IN THE FIELD OF FEDERATED LEARNING AND HOW CAN YOU WORK WITH TNO?