Privacy-preserving ML (federated, DP, on-device) + DSA review

Interview questions to prep

1. Do you sort by start or by end? Defend the choice based on the invariant you need.
2. Walk through merge / overlap detection: what's your condition for 'overlapping'?
3. How does complexity break down: O(n log n) sort + O(n) sweep — can you do better in any case?

Interview questions to prep

1. What levers do you pull when accuracy is great but latency misses the budget?
2. Walk through where you'd add caching in a RAG + LLM pipeline to halve P99.

Interview questions to prep

1. How would you design a cascade: cheap model first, expensive only when needed?
2. What's the right verifier for the cheap model's output — and when does it dominate cost?

Interview questions to prep

1. Walk through cold-start strategies for new users vs new items.
2. Compare bandit-based exploration vs content-based bridges for cold start — when does each fit?

Interview questions to prep

1. When would you reach for federated learning vs differential privacy vs on-device inference?
2. What's the accuracy cost of DP-SGD at typical ε values, and how do you decide if it's acceptable?

Interview questions to prep

1. How would you remove PII from training data without destroying signal?
2. Compare hashing vs tokenization vs full redaction for PII fields — when does each fit?

Interview questions to prep

1. Explain differential privacy in one minute — what does ε mean?
2. How does DP-SGD differ from regular SGD, and what does it cost in accuracy?

Interview questions to prep

1. When is federated learning the right choice over centralized training?
2. What are the systems-side challenges in federated learning beyond the algorithm?

Interview questions to prep

1. What is a model-extraction attack, and how do you mitigate it?
2. How would you defend a public ML API from membership-inference attacks?