Are privacy-preserving techniques actually being used in production ML systems? [D]

I've been reading more about privacy-preserving ML approaches such as differential privacy, federated learning, and on-device inference.

The research literature is fairly active, but I'm curious about real-world adoption.

For those working in industry:

• Are these techniques being deployed in production?
• What were the biggest engineering challenges?
• Did privacy requirements significantly impact model performance or infrastructure costs?
• Are there specific use cases where privacy-preserving approaches have proven especially valuable?

Interested in hearing both success stories and cases where the tradeoffs made adoption difficult.