Privacy preserving federated learning is a thing - essentially you train a local model and send the weight updates back to Google rather than the data itself....but also it's early days so who knows what vulnerabilities may exist
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There's is a huge difference though.
That being one is making hardware and the other is copying books into your training pipeline though
The copy occurs in the dataset preparation.