Federated AI belongs in the categories of Artificial Intelligence, Big Data and Smart Data, and Cybercrime and Cybersecurity. This term describes a particular way in which Artificial Intelligence (AI) works with large amounts of data without the data needing to be collected in a central location.
Instead of storing all data on one server, many individual computers or devices each learn from their own data. The results or „learning progress“ are then aggregated without sharing the sensitive original data. This increases security and protects personal information, for example.
A typical example: Several hospitals want to jointly train an AI for disease diagnosis. However, due to data protection regulations, patient data may not be stored centrally. With Federated AI, each hospital can train the AI with its own data. Afterwards, only the newly gained insights, not the actual patient data, are shared and aggregated.
Federated AI makes it possible to deploy modern AI solutions in sensitive areas, such as healthcare or finance, without compromising data protection and security. This is a significant advantage, especially in an era of increasing cybercrime.
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