Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …

Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Federated learning (FL), as a distributed machine learning setting, has received
considerable attention in recent years. To alleviate privacy concerns, FL essentially …

In this work, the federated learning mechanism is introduced into the deep learning of
medical models in Internet of Things (IoT)-based healthcare system. Cryptographic …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

While federated learning (FL) is a well-known privacy-preserving (PP) solution, recent
studies demonstrate that it still has privacy problems and vulnerabilities, particularly in the …

Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …

As an emerging sensing data collection paradigm, mobile crowdsensing (MCS) enjoys good
scalability and low deployment cost but raises privacy concerns. In this paper, we propose a …

Federated learning (FL) preserves data privacy by exchanging gradients instead of local
training data. However, these private data can still be reconstructed from the exchanged …

U-shaped networks have become prevalent in various medical image tasks such as
segmentation, and restoration. However, most existing U-shaped networks rely on …