High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions
| Autor: | Jörg Rähenfürher, Andrea Bommert, Michel Lang, Florian Pfisterer, Bernd Bischl, Xudong Sun |
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| Rok vydání: | 2019 |
| Předmět: |
Clustering high-dimensional data
Federated Architecture Process (engineering) Computer science Model selection Bayesian optimization 02 engineering and technology computer.software_genre 01 natural sciences 010104 statistics & probability 020204 information systems 0202 electrical engineering electronic engineering information engineering Differential privacy Train Data mining 0101 mathematics computer Selection (genetic algorithm) |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030295158 IntelliSys (1) |
| DOI: | 10.1007/978-3-030-29516-5_48 |
| Popis: | A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can not leave the site it is situated on and it is forbidden to carry out training algorithms on remote data sites due to either technical or privacy and trust concerns. To carry out a clinical research in this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper. Compared to federated learning, which is optimizing the model parameters directly by carrying out training across all data sites, RFMS trains model parameters only on one local data site but optimizes hyper parameters across other data sites jointly since hyper-parameters play an important role in machine learning performance. The aim is to get a Pareto optimal model with respective to both local and remote unseen prediction losses, which could generalize well across data sites. In this work, we specifically consider high dimensional data with different distributions over data sites. As an initial investigation, Bayesian Optimization especially multi-objective Bayesian Optimization is used to guide an adaptive hyper-parameter optimization process to select models under the RFMS scenario. Empirical results shows that solely using the local data site to tune hyper-parameters generalizes poorly across data sites, compared to methods that utilize the local and remote performances. Furthermore, in terms of hypervolumes, multi-objective Bayesian Optimization algorithms show increased performance across multiple data sites among other candidates. |
| Databáze: | OpenAIRE |
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