•1 min read•from Machine Learning
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.
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