A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction
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 f...
| मुख्य लेखकों: | , , , , , , |
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| स्वरूप: | लेख |
| भाषा: | English |
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MDPI AG
2023-03-01
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| श्रृंखला: | Sensors |
| विषय: | |
| ऑनलाइन पहुंच: | https://www.mdpi.com/1424-8220/23/6/2957 |
| _version_ | 1827747755188027392 |
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| author | Ivanoe De Falco Antonio Della Cioppa Tomas Koutny Martin Ubl Michal Krcma Umberto Scafuri Ernesto Tarantino |
| author_facet | Ivanoe De Falco Antonio Della Cioppa Tomas Koutny Martin Ubl Michal Krcma Umberto Scafuri Ernesto Tarantino |
| author_sort | Ivanoe De Falco |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula>, 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. |
| first_indexed | 2024-03-11T05:56:45Z |
| format | Article |
| id | doaj.art-e8c74c5d348a4eb4927f6fd9cccde7ec |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:56:45Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e8c74c5d348a4eb4927f6fd9cccde7ec2023-11-17T13:43:55ZengMDPI AGSensors1424-82202023-03-01236295710.3390/s23062957A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose PredictionIvanoe De Falco0Antonio Della Cioppa1Tomas Koutny2Martin Ubl3Michal Krcma4Umberto Scafuri5Ernesto Tarantino6ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, ItalyICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, ItalyDepartment of Computer Science and Engineering, New Technologies for Information Society, University of West Bohemia, Technicka 18, 330 01 Pilsen, Czech RepublicDepartment of Computer Science and Engineering, University of West Bohemia, Technicka 18, 330 01 Pilsen, Czech RepublicDiabetology Center, First Department of Internal Medicine, University Hospital Pilsen, Alej Svobody 923/80, 323 00 Pilsen, Czech RepublicICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, ItalyICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, ItalyIn 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula>, 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.https://www.mdpi.com/1424-8220/23/6/2957federated learningevolutionary algorithmsinterpretable machine learningdiabetes |
| spellingShingle | Ivanoe De Falco Antonio Della Cioppa Tomas Koutny Martin Ubl Michal Krcma Umberto Scafuri Ernesto Tarantino A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction Sensors federated learning evolutionary algorithms interpretable machine learning diabetes |
| title | A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction |
| title_full | A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction |
| title_fullStr | A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction |
| title_full_unstemmed | A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction |
| title_short | A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction |
| title_sort | federated learning inspired evolutionary algorithm application to glucose prediction |
| topic | federated learning evolutionary algorithms interpretable machine learning diabetes |
| url | https://www.mdpi.com/1424-8220/23/6/2957 |
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