Florian Hartmann
Authored Publications
Sort By
Preview abstract
Cascades are a common type of machine learning system where a larger, remote model can be queried if a local model is not able to handle a user’s query by itself. They are becoming an increasingly popular choice of a design for Large Language Models (LLMs) serving stacks due to their ability to preserve task performance, while dramatically reducing inference costs. However, applying cascade systems in situations where the local model has access to sensitive data constitutes a significant privacy risk for users since any such data could be forwarded to the remote model. In this work, we show the feasibility of applying cascade systems in such setups, equipping the local model with privacy-preserving techniques that reduce the risk of leaking private information when querying the remote model. To analyze the privacy of such a setup, we introduce a novel privacy measure that quantifies sensitive information leakage. We then propose a system that leverages the recently introduced social learning paradigm in which LLMs collaboratively learn from each other by exchanging natural language and demonstrate on several datasets that our methods minimize the privacy loss while at the same time improving task performance compared to a non-cascade baseline.
View details
