What is the main advantage of federated learning in machine learning?

The main advantage of federated learning is that it enables the training of machine learning models without the need to share raw data between devices. This approach allows models to be trained locally on user devices while keeping the sensitive data on those devices, thereby enhancing privacy and data security.

By aggregating the learned updates or model parameters instead of the data itself, federated learning helps organizations comply with data protection regulations, such as GDPR, while still benefiting from collective insights across distributed datasets. This method reduces the risk of data breaches and is particularly beneficial in scenarios where data privacy is paramount, such as in healthcare or financial applications.

In contrast, options that suggest centralizing data collection or sharing raw data between devices undermine these privacy principles and would not be aligned with the core objectives of federated learning. Similarly, while federated learning may simplify some aspects of model deployment, this is not its primary advantage compared to the key benefit of enhanced privacy and security in data handling.