Recently, there has been the development of Split Learning, a framework for distributed computation where model components are split between the client and server (Vepakomma et al., 2018b). As Split Learning scales to include many different model components, there needs to be a method of matching client-side model components with the best server-side model components. A solution to this problem was introduced in the ExpertMatcher (Sharma et al., 2019) framework, which uses autoencoders to match raw data to models. In this work, we propose an extension of ExpertMatcher, where matching can be performed without the need to share the client's raw data representation. The technique is applicable to situations where there are local clients and centralized expert ML models, but the sharing of raw data is constrained.
@article{arxiv.1910.03731,
title = {ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations},
author = {Vivek Sharma and Praneeth Vepakomma and Tristan Swedish and Ken Chang and Jayashree Kalpathy-Cramer and Ramesh Raskar},
journal= {arXiv preprint arXiv:1910.03731},
year = {2019}
}
Comments
In NeurIPS Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, 2019