How might federated learning be used to address privacy concerns in AI model training?

By aggregating model updates on the local devices.
By sharing user data with third parties.
By training models on centralized servers.
By utilizing public datasets.

Federated learning allows model training to occur on local devices, keeping user data decentralized and private. Model updates are aggregated without sharing raw data, thus addressing privacy concerns.

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Imagine a situation where an AI system responsible for managing critical infrastructure is found to have vulnerabilities that might be exploited by malicious actors. What would be your immediate steps to mitigate risks and ensure continuity of services?

In a Convolutional Neural Network (CNN), the operation responsible for reducing the spatial size of the representation and reducing the number of parameters is known as _______.

How might federated learning be used to address privacy concerns in AI model training?

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