Search Results for author: Seyit A. Camtepe

Found 6 papers, 3 papers with code

Vertical Federated Learning: Taxonomies, Threats, and Prospects

no code implementations3 Feb 2023 Qun Li, Chandra Thapa, Lawrence Ong, Yifeng Zheng, Hua Ma, Seyit A. Camtepe, Anmin Fu, Yansong Gao

In a number of practical scenarios, VFL is more relevant than HFL as different companies (e. g., bank and retailer) hold different features (e. g., credit history and shopping history) for the same set of customers.

Federated Learning

A Game-Theoretic Approach for AI-based Botnet Attack Defence

no code implementations4 Dec 2021 Hooman Alavizadeh, Julian Jang-Jaccard, Tansu Alpcan, Seyit A. Camtepe

The new generation of botnets leverages Artificial Intelligent (AI) techniques to conceal the identity of botmasters and the attack intention to avoid detection.

Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things

1 code implementation3 Mar 2021 Yansong Gao, Minki Kim, Chandra Thapa, Sharif Abuadbba, Zhi Zhang, Seyit A. Camtepe, Hyoungshick Kim, Surya Nepal

Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning training without accessing raw data on clients or end devices.

BIG-bench Machine Learning Federated Learning

Advancements of federated learning towards privacy preservation: from federated learning to split learning

no code implementations25 Nov 2020 Chandra Thapa, M. A. P. Chamikara, Seyit A. Camtepe

In practical scenarios, all clients do not have sufficient computing resources (e. g., Internet of Things), the machine learning model has millions of parameters, and its privacy between the server and the clients while training/testing is a prime concern (e. g., rival parties).

BIG-bench Machine Learning Federated Learning

End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things

1 code implementation30 Mar 2020 Yansong Gao, Minki Kim, Sharif Abuadbba, Yeonjae Kim, Chandra Thapa, Kyuyeon Kim, Seyit A. Camtepe, Hyoungshick Kim, Surya Nepal

For learning performance, which is specified by the model accuracy and convergence speed metrics, we empirically evaluate both FL and SplitNN under different types of data distributions such as imbalanced and non-independent and identically distributed (non-IID) data.

Federated Learning

Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?

1 code implementation16 Mar 2020 Sharif Abuadbba, Kyuyeon Kim, Minki Kim, Chandra Thapa, Seyit A. Camtepe, Yansong Gao, Hyoungshick Kim, Surya Nepal

We observed that the 1D CNN model under split learning can achieve the same accuracy of 98. 9\% like the original (non-split) model.

Privacy Preserving

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