BÜYÜK VERİLERDE GİZLİLİK TABANLI YAKLAŞIM: FEDERE ÖĞRENME

Yıl 2019, Cilt: 3 Sayı: 3, 297 - 304, 31.12.2019

Ahmet Ali Süzen , Kiyas Kayaalp

Öz

Bu çalışmada dağıtık yapılardaki büyük verilerden gizlilik tabanlı makine öğrenmesi uygulamaları geliştirilmesi için federe öğrenme biçimi anlatılmıştır. Federe öğrenme eğitim verilerini yerelde tutarken, cep telefonları ve IoT (Internet of Things) cihazları gibi kaynakları kısıtlı uç aygıtların tahmin için paylaşılan bir model öğrenmesini sağlar. Federe öğrenme büyük ve heterojen ağlarda modellerin istatiksel eğitimlerini içerir. Bu dağıtık yapı içerisinde temel amaç toplam kayıp fonksiyon değerini minimize edebilmektir. Dağıtık yerel cihazlarda modelleri federe öğrenme ile eğitmedeki istatistiksel ve sistematik zorluklar, federe öğrenmenin gerçek dünyaya uygulanmasını zorlaştırmaktadır. Zorlukların çözümü ile ilgili yeni yaklaşımlar ve algoritmalar önerilmektedir. Elde edilen sonuçlar doğrultusunda federe öğrenmenin uygulanması ile merkezi yaklaşım, gizlilik, güvenlik, düzenleyici ve ekonomik olarak faydalar sağlayacağı öngörülmektedir.

Anahtar Kelimeler

Federe Öğrenme, Makine Öğrenmesi, Gizlilik, Büyük Veri

A PRIVACY-BASED APPROACH IN BIG DATA: FEDERATED LEARNING

Öz

This study describes the Federated way of learning to develop privacy-based machine learning applications from large data in distributed structures. Federated learning allows learning a shared model for forecasting resource-restricted end devices, such as mobile phones and IoT (Internet of Things) devices, while keeping training data locally. Federated learning involves statistical training of models in large and heterogeneous networks. The main purpose of this distributed structure is to minimize the total loss function value. The statistical and systematic challenges in federated learning and training in distributed local devices make it difficult to apply federated learning to the real world. It has been proposed new approaches and algorithms for solving challenges. In lights with the results achieved, it is envisaged that the implementation of federated learning and the central approach will bring benefits in terms of privacy, security, regulatory and economic terms.

Anahtar Kelimeler

Big Data, Federated Learning, , Machine Learning, Privacy

Kaynakça

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