Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

Purpose This study explores privacy challenges in recommender systems (RSs) and how
they have leveraged privacy-preserving technology for risk mitigation. The study also …

Federated Learning (FL) is an efficient and secure machine learning technique designed for
decentralized computing systems such as fog and edge computing. Its learning process …

With federated learning, one of the most notable features is that it can update global model
parameter without using the users' local data. However, various security and privacy …

This work aims to analyze malicious communication behaviors that pose a threat to the
security of digital twins (DTs) and safeguard user privacy. A unified and integrated …

Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …

In digital intelligence era, the authenticity of data and the privacy protection of data sharing
and multiparty collaborative computing are key factors in building a good credit evaluation …

The local model poisoning attack is an attack to manipulate the shared local models during
the process of distributed learning. Existing defense methods are passive in the sense that …

With the development of information technology, massive and heterogeneous consumer
electronic products can access the network. These products may engage third-party servers …

Systolic arrays are an integral part of many modern machine learning (ML) accelerators due
to their efficiency in performing matrix multiplication that is a key primitive in modern ML …