Yazar "Kuzlu, Murat" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    Federated learning: Overview, strategies, applications, tools and future directions
    (Elsevier Ltd, 2024) Yurdem, Betul; Kuzlu, Murat; Gullu, Mehmet Kemal; Catak, Ferhat Ozgur; Tabassum, Maliha
    Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications. © 2024 The Author(s)
  • Küçük Resim Yok
    Öğe
    Serverless federated learning: Decentralized spectrum sensing in heterogeneous networks
    (Elsevier, 2025) Catak, Ferhat Ozgur; Kuzlu, Murat; Dalveren, Yaser; Ozdemir, Gokcen
    Federated learning (FL) has gained more popularity due to the increasing demand for robust and efficient mechanisms to ensure data privacy and security during collaborative model training in the concept of artificial intelligence/machine learning (AI/ML). This study proposes an advanced version of FL without the central server, called a serverless or decentralized federated learning framework, to address the challenge of cooperative spectrum sensing in non-independent and identically distributed (non-IID) environments. The framework leverages local model aggregation at neighboring nodes to improve robustness, privacy, and generalizability. The system incorporates weighted aggregation based on distributional similarity between local datasets using Wasserstein distance. The results demonstrate that the proposed serverless federated learning framework offers a satisfactory performance in terms of accuracy and resilience.