ISSN :2582-9793

A Comparison of Methods for Neural Network Aggregation

Original Research (Published On: 09-May-2023 )
A Comparison of Methods for Neural Network Aggregation
DOI : 10.54364/AAIML.2023.1160

Aviv Segev and John Pomerat

Adv. Artif. Intell. Mach. Learn., 3 (2):1012-1024

Aviv Segev : University of South Alabama

John Pomerat : University of South Alabama

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DOI: 10.54364/AAIML.2023.1160

Article History: Received on: 03-Apr-23, Accepted on: 26-Apr-23, Published on: 09-May-23

Corresponding Author: Aviv Segev

Email: segev@southalabama.edu

Citation: John Pomerat and Aviv Segev (2023). A Comparison of Methods for Neural Network Aggregation. Adv. Artif. Intell. Mach. Learn., 3 (2 ):1012-1024


Abstract

    

Deep learning has been successful in the theoretical aspect. For deep learning to succeed in industry, we need to have algorithms capable of handling many inconsistencies appearing in real data. These inconsistencies can have large effects on the implementation of a deep learning algorithm. Artificial Intelligence is currently changing the medical industry. However, receiving authorization to use medical data for training machine learning algorithms is a huge hurdle. A possible solution is sharing the data without sharing the patient information. We propose a multi-party computation protocol for the deep learning algorithm. The protocol enables to conserve both the privacy and the security of the training data. Three approaches of neural networks assembly are analyzed: transfer learning, average ensemble learning, and series network learning. The results are compared to approaches based on data-sharing in different experiments. We analyze the security issues of the proposed protocol. Although the analysis is based on medical data, the results of multi-party computation of machine learning training are theoretical and can be implemented in multiple research areas.

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