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Research on Online Social Network Information Leakage-Tracking Algorithm Based on Deep Learning.

Computational intelligence and neuroscience
The rapid iteration of information technology makes the development of online social networks increasingly rapid, and its corresponding network scale is also increasingly large and complex. The corresponding algorithms to deal with social networks an...

Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration.

Scientific reports
Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriente...

Generalized genomic data sharing for differentially private federated learning.

Journal of biomedical informatics
The success behind Machine Learning (ML) methods has largely been attributed to the quality and quantity of the available data which can spread across multiple owners. A Federated Learning (FL) from distributed datasets often provides a reliable solu...

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment.

Computational intelligence and neuroscience
The Industrial Internet of Things (IIoT) has received significant attention from several leading industries like agriculture, mining, transport, energy, and healthcare. IIoT acts as a vital part of Industry 4.0 that mainly employs machine learning (M...

Personalized On-Device E-Health Analytics With Decentralized Block Coordinate Descent.

IEEE journal of biomedical and health informatics
Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating. Nowadays, enhancement on medical diagnosis via machine learning models has been highly effective in many aspects of e-health analy...

A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data.

Scientific reports
Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI ...

Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.

Scientific reports
The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specific training) comes at the cost of performance degradation when applied to external patient cohorts. ...

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning.

Computational intelligence and neuroscience
A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person's abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-atta...

Federated Learning in Medical Imaging: Part I: Toward Multicentral Health Care Ecosystems.

Journal of the American College of Radiology : JACR
With recent developments in medical imaging facilities, extensive medical imaging data are produced every day. This increasing amount of data provides an opportunity for researchers to develop data-driven methods and deliver better health care. Howev...

DACFL: Dynamic Average Consensus-Based Federated Learning in Decentralized Sensors Network.

Sensors (Basel, Switzerland)
Federated Learning (FL) is a privacy-preserving way to utilize the sensitive data generated by smart sensors of user devices, where a central parameter server (PS) coordinates multiple user devices to train a global model. However, relying on central...