Abstract Artificial Neural Networks (ANNs) have become the backbone of many real-world
applications, including distributed applications relying on Federated Learning (FL) …

Deep learning has shown its great potential in real‐world applications. However, users
(clients) who want to use deep learning applications need to send their data to the deep …

This paper introduces SafeML, a distributed machine learning framework that can address
privacy and Byzantine robustness concerns during model training. It employs secret sharing …

This paper introduces a distributed deep learning framework called TrustDDL crafted to
address privacy and Byzantine robustness concerns across the training and inference …

Garbled circuits are the leading cryptographic techniques for constant-round secure two-
party computation (S2PC). Classical constructions of Garbled circuits (GC) utilize 4 …

Neural networks have a wide range of promise for image prediction, but in the current setting
of neural networks as a service, the data privacy of the parties involved in prediction raises …

Deep learning (DL) can be considered as a powerful tool in different fields and for different
applications but its importance raised the concern about privacy, security, and defense …

Deep learning has made remarkable research advancements and wide-ranging
applications in the domains of computer vision, multimodal, natural language processing …

With the rapid expansion of data, increasing computational power, and the complexity of
high-dimensional datasets, it is of utmost importance to integrate a novel privacy-preserving …