From the course: AI Security and Responsible AI Practices by Pearson

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Delving into federated learning

Delving into federated learning

From the course: AI Security and Responsible AI Practices by Pearson

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Delving into federated learning

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The final approach that we're going to discuss in this section is federated learning. Federated learning is a collaborative process in machine learning that enables model training across multiple decentralized devices or servers while keeping the data localized. This technique is very relevant in areas like cybersecurity, where the data privacy and security are important. The basic concept of federated learning is that it is a shared model, is trained across multiple devices, so we have multiple computers or nodes, and each is holding local data samples. Those are stored here locally. And only they send updates of the data to the central server. Since the raw data never leaves the original location, which is the node, federated learning improves the data privacy and security. This is a perfect approach for very sensitive data applications and ensures efficiency, and it has a potential for scalability. So you can transfer large datasets to a central location, and then those could be…

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