Collective Machine Learning is primarily at home in the fields of Artificial Intelligence, Big Data and Smart Data, as well as Industry and Industry 4.0. It describes a method in which many different computers, machines, or even companies learn together from data without having to share their confidential information directly with each other.
Imagine several hospitals want to develop an artificial intelligence that can better detect diseases. Each hospital has a lot of data, but it cannot be shared due to data protection reasons. With collective machine learning, each hospital can train the model locally on its own data. The resulting learning outcomes (for example, patterns and suggestions) are then anonymised and evaluated collectively to create a shared, improved AI model. This way, everyone benefits from collective knowledge without disclosing sensitive patient data.
The great advantage: Companies or organisations benefit from more data and better results, while data protection and confidentiality remain protected. Collective machine learning is therefore an important building block for innovative applications – for example in medicine, energy management or production optimisation.
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