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A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2023 Mar 30
PMID 36991668
Authors
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Abstract

In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F1, and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place.

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References
1.
Maniruzzaman M, Rahman M, Ahammed B, Abedin M . Classification and prediction of diabetes disease using machine learning paradigm. Health Inf Sci Syst. 2020; 8(1):7. PMC: 6942113. DOI: 10.1007/s13755-019-0095-z. View

2.
Papatheodorou K, Banach M, Bekiari E, Rizzo M, Edmonds M . Complications of Diabetes 2017. J Diabetes Res. 2018; 2018:3086167. PMC: 5866895. DOI: 10.1155/2018/3086167. View

3.
Rudin C . Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell. 2022; 1(5):206-215. PMC: 9122117. DOI: 10.1038/s42256-019-0048-x. View

4.
Joseph L, Joseph E, Prasad R . Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture. Comput Biol Med. 2022; 151(Pt A):106178. DOI: 10.1016/j.compbiomed.2022.106178. View

5.
Marling C, Bunescu R . The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020. CEUR Workshop Proc. 2021; 2675:71-74. PMC: 7881904. View