Collective machine learning is particularly at home in the fields of artificial intelligence, big data and smart data as well as Industry and Factory 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 that several hospitals want to develop artificial intelligence that can better recognise diseases. Each hospital has a lot of data that cannot be shared for data protection reasons. With collective machine learning, each hospital can train the model locally on its own data. The resulting learning outcomes (e.g. patterns and suggestions) are then anonymised and evaluated collectively to create a joint, improved AI model. This allows everyone to utilise the combined knowledge without disclosing sensitive patient data.
The big advantage: companies or organisations benefit from more data and better results, while data protection and confidentiality are maintained. 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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