State-of-the-art edge devices are capable of not only inferring machine learning (ML)
models but also training them on the device with local data. When this local data is sensitive …

Federated learning (FL) refers to a system of training and stabilizing local machine learning
models at the global level by aggregating the learning gradients of the models. It reduces …

Generation of massive data in the digital age leads to possible violations of individual
privacy. The search for personal data becomes an increasingly recurrent exposure today …

Federated Learning (FL), as an emerging form of distributed machine learning (ML), can
protect participants' private data from being substantially disclosed to cyber adversaries. It …

Recommender Systems (RSs) are valuable technologies that help users in their decision-
making process. Generally, RSs are designed with the assumption that a central server …

In traditional recommender systems, we often build models based on a centralized storage
of user data, which however will lead to user privacy concerns and risks. In this paper, we …

Federated recommender systems can serve users with suitable item recommendations
while preserving their privacy, but most current works cannot serve non-participant users …

Rapid advances in sensing technology have enabled the collection of vast amounts of data
from manufacturing operations, which has expedited big-data-driven innovations in …

The framework of differential privacy protects an individual's privacy while publishing query
responses on congregated data. In this work, a new noise addition mechanism for …

Differential privacy (DP) techniques can be applied to the federated learning model to
statistically guarantee data privacy against inference attacks to communication among the …