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You're tasked with building a system that generates personalized exercise recommendations based on user's text descriptions of their fitness goals and images of their current physical condition. Due to privacy concerns, you cannot directly access the user's raw images or text after initial processing. What technique can allow you to continue to train the model while respecting these privacy constraints?.
Correct Answer: A
Federated learning allows training a model across multiple decentralized devices or servers holding local data samples, without exchanging them. This is perfect for privacy-sensitive scenarios as the raw data remains on the user's device. Transfer learning relies on pre- trained models, data augmentation modifies existing data, GANs generate new data (but still require initial data access), and reinforcement learning optimizes actions through interaction with an environment.