Model-Free-Communication Federated Learning: Framework and application to Precision Medicine

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43969347" target="_blank" >RIV/49777513:23520/24:43969347 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1746809423008492" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1746809423008492</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2023.105416" target="_blank" >10.1016/j.bspc.2023.105416</a>

Result language

angličtina

Original language name

Model-Free-Communication Federated Learning: Framework and application to Precision Medicine

Original language description

The problem of executing machine learning algorithms over data while complying with data privacy is highly relevant in many application areas, including medicine in general and Precision Medicine in particular. In this paper, an innovative framework for Federated Learning is proposed that allows performing machine learning and effectively tackling the issue of data privacy while taking a step towards security during communication. Unlike the standard federated approaches where models should travel on the communication networks and would be subject to possible cyberattacks, the models proposed by our framework do not need to travel, thus moving in the direction of security improvement. Another very appealing feature is that it can be used with any machine learning algorithm provided that, during the learning phase, the model updating does not depend on the input data. To show its effectiveness, the learning process is here accomplished by an Evolutionary Algorithm, namely Grammatical Evolution, thus also obtaining explicit knowledge that can be provided to the domain experts to justify the decisions made. As a test case, glucose values prediction for a number of patients with type 1 diabetes is considered and is tackled as a classification problem, the goal being to predict for any future value a possible range. Finally, a comparison of the performance of the proposed framework is performed against that of a non-Federated Learning approach.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

1746-8108

Volume of the periodical

87B

Issue of the periodical within the volume

105416

Country of publishing house

GB - UNITED KINGDOM

Number of pages

17

Pages from-to

1-17

UT code for WoS article

001083187800001

EID of the result in the Scopus database

2-s2.0-85171788413