ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
By Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Kenglun Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar. 2019
11 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, ML Systems & Hardware
How researchers see this paper
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In Artificial Intelligence
- Martin Jaggi
- Qiang Yang
- Jakub Konecný
- Peter Kairouz
- Peter Richtárik
- Ananda Theertha Suresh
- Jianyu Wang
- Sebastian U. Stich
In ML Systems & Hardware
- Jianyu Wang
- Martin Jaggi
- Sebastian U. Stich
- Oluwasanmi Koyejo
- Jakub Konecný
- Gauri Joshi
- Medhi Bennis
- Zachary B. Charles
Citations in the corpus
- Advances and Open Problems in Federated Learning (2019, methodology): “Split learning also enables matching client-side model components with the best server-side model components for automating model selection as shown in the ExpertMatcher [353].”