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Found 6 papers, 3 papers with code
Vertical Federated Learning: Taxonomies, Threats, and Prospects
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.
A Game-Theoretic Approach for AI-based Botnet Attack Defence
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
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.
Advancements of federated learning towards privacy preservation: from federated learning to split learning
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).
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
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.
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
We observed that the 1D CNN model under split learning can achieve the same accuracy of 98. 9\% like the original (non-split) model.
