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.
The new generation of botnets leverages Artificial Intelligent (AI) techniques to conceal the identity of botmasters and the attack intention to avoid detection.
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.
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).
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.
We observed that the 1D CNN model under split learning can achieve the same accuracy of 98. 9\% like the original (non-split) model.