Mobil İletişim Ağları Alanında Güvenli ve Mahremiyet Odaklı Federe Öğrenme Üzerine Bir Araştırma

Abstract

Bu çalışmada, veri gizliliği ile iş birliğine dayalı makine öğrenimi arasındaki boşluğu dolduran güvenlik odaklı, gizlilik artırılmış federe öğrenme için en son çözümleri ele alıyoruz. Analizimiz, mevcut metodolojilerin kapsamlı bir karşılaştırmalı değerlendirmesini sunarak, mevcut yaklaşımların güçlü ve zayıf yönlerine ışık tutuyor. Yeni bakış açıları sunarak, güvenli federe öğrenmenin sınırlarını zorlamayı ve öğrenme verimliliğinden ödün vermeden veri korumasını artıran teknikleri keşfetmeyi amaçlıyoruz. Ayrıca, merkezi olmayan sistemlerde ölçeklenebilir, mahremiyet odaklı mekanizmaların önemini vurgulayan ortaya çıkan zorluklar ve fırsatlara dikkat çekiyoruz. Federe öğrenme, sağlık hizmetleri, finans ve Nesnelerin Interneti gibi çeşitli sektörlerde hız kazanmaya devam ederken, çalışmamız, yenilik ve gelişim için önemli alanları belirleyerek, gelecekteki araştırmalar için bir temel niteliği taşıyor. Bu ileriye dönük yaklaşım, federe öğrenmenin hem mevcut hem de gelecekteki güvenlik kaygılarını ele alarak, gizliliğe duyarlı uygulamalar için güvenilir ve sağlam bir çözüm olarak gelişmeye devam etmesini sağlıyor.

Keywords

• Federe öğrenme
• mahremiyet
• güvenli birleştirme
• homomorfik şifreleme
• çok taraflı güvenli hesaplama

Project Number

5169902

A Survey On Security and Privacy Aspects and Solutions for Federated Learning in Mobile Communication Networks

Abstract

In this study, we delve into cutting-edge solutions for security-centric, privacy-enhanced federated learning, a rapidly evolving area of research that bridges the gap between data privacy and collaborative machine learning. Our analysis offers a comprehensive comparative evaluation of existing methodologies, shedding light on the strengths and limitations of current approaches. By introducing new perspectives, we aim to push the boundaries of secure federated learning, exploring techniques that enhance data protection without compromising learning efficiency. Additionally, we highlight emerging challenges and opportunities in the field, emphasizing the importance of scalable, privacy-preserving mechanisms in decentralized systems. As federated learning continues to gain traction across various sectors such as healthcare, finance, and IoT, our study serves as a foundation for future research, identifying key areas for innovation and improvement. This forward-looking approach ensures that federated learning can continue to evolve as a trustworthy and robust solution for privacy-sensitive applications, addressing both current and future security concerns.

Keywords

• Federated learning
• privacy
• secure aggregation
• homomorphic encryption
• secure multi-party computation

Supporting Institution

TUBITAK

Project Number

5169902

Thanks

This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) through the 1515 Frontier Research and Development Laboratories Support Program under Project 5169902.

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