As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

Deep learning methods have achieved significant results in various fields. Due to the
success of these methods, many researchers have used deep learning algorithms in …

Federated learning (FL) is an emerging paradigm that enables multiple organizations to
jointly train a model without revealing their private data to each other. This paper studies {\it …

Smart contracts are applications that execute on blockchains. Today they manage billions of
dollars in value and motivate visionary plans for pervasive blockchain deployment. While …

Blockchain, an emerging paradigm of secure and shareable computing, is a systematic
integration of 1) chain structure for data verification and storage, 2) distributed consensus …

Self‐sovereign identity is the next evolution of identity management models. This survey
takes a journey through the origin of identity, defining digital identity and progressive …

Privacy-preserving multi-party machine learning allows multiple organizations to perform
collaborative data analytics while guaranteeing the privacy of their individual datasets …

Federated learning is a recently proposed distributed machine learning paradigm for privacy
preservation, which has found a wide range of applications where data privacy is of primary …

Abstract Machine learning classification is used in numerous settings nowadays, such as
medical or genomics predictions, spam detection, face recognition, and financial predictions …

Recent deep face recognition models proposed in the literature utilized large-scale public
datasets such as MS-Celeb-1M and VGGFace2 for training very deep neural networks …