# ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations

arXiv: https://arxiv.org/abs/1910.03731 · HTML: https://mlsanity.com/paper/1910.03731/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [Federated Two-stage Learning with Sign-based Voting](https://mlsanity.com/paper/2112.05687/) (2021)
- [Towards a Universal Gating Network for Mixtures of Experts](https://mlsanity.com/paper/2011.01613/) (2020)

## Abstract (arXiv)

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.