Geliştirilen Dağıtılmış Makine Öğrenimi Modellerinin Blok Zincirde Depolanması

Year 2023, Volume: 9 Issue: 2, 248 - 263, 31.08.2023

Remzi Gürfidan , Mevlüt Ersoy

Abstract

Bu çalışmada farklı lokasyonlardan toplanan hava kirliliği bilgilerini içeren bir veri seti kullanılmıştır. Bu veri seti Seul şehrinin 25 farklı ilçesine ait hava kirliliği ölçüm bilgileri ve sonuç bilgilerini içermektedir. Benzer bölgelerden elde edilen değerler tüm veri seti içerisinden filtrelenerek bu bölgelere has veri seti oluşturulmuştur. Sonraki işlem basamağı olarak, kendi bölgelerinde elde edilen değerler ile eğitilip model kaydedilmiştir. Bu model kendi alanlarında elde edilen veriler ile test edildiğinde oldukça başarılı sonuçlar ortaya koymaktadır. Fakat farklı bölgelerden elde edilen ve bu bölgeler ile benzeşmeyen veriler ile test edildiğinde model sınıflama başarısı oldukça düşük kalmıştır. Çalışmanın amacı bu problem durumuna çözüm olabilecek olan dağıtık öğrenme yöntemi ile klasik makine öğrenmesine kıyasla daha başarılı sonuçlar elde edilebileceğini göstermektir. Bunun yanında Dağıtık Öğrenme kısmında model birleştirme ve toplama esnasındaki güvenlik sorunlarının da göz ardı edilmemelidir. Bu sebeple model dağıtımı sırasında kötü niyetli saldırılara karşı blockchain tabanlı sağlam bir yaklaşım önerilmiştir.

Keywords

Dağıtık öğrenme, makine öğrenmesi, blok zinciri, modelin güvenli depolanması

Supporting Institution

Süleyman Demirel Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

FDK-2022-8719

Thanks

Çalışma Süleyman Demirel Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından FDK-2022-8719 proje numarası ile desteklenmiştir. Süleyman Demirel Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi'ne desteklerinden dolayı teşekkür ederiz. Bu makale ilk yazarın Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü'nde kabul edilen "Blokzincir Tabanlı Dağitik Modelleri İçin Hesaplama Altyapilarının Gerçekleştirilmesi" adlı doktora tezinden üretilmiştir.

Storage of Developed Distributed Machine Learning Models on Blockchain

Abstract

In this study, a data set containing air pollution information collected from different locations was used. This dataset contains air pollution measurement information and results information for 25 different districts of Seoul city. The values obtained from similar regions were filtered from the entire data set and a data set specific to these regions was created. As the next step, the model was trained with the values obtained in its own regions and the model was recorded. When this model is tested with the data obtained in its own fields, it shows very successful results. However, when tested with data obtained from different regions and dissimilar to these regions, the model classification success remained very low. The aim of the study is to show that more successful results can be obtained with the distributed learning method, which can be a solution to this problem situation, compared to classical machine learning. In addition, security problems during model aggregation and aggregation should not be ignored in the Distributed Learning section. For this reason, a robust blockchain-based approach is proposed against malicious attacks during model deployment.

Keywords

Distributed learning, machine learning, blockchain, secure storage of model

Project Number

FDK-2022-8719

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