AIMC Topic: Privacy

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

Computational intelligence and neuroscience Jun 8, 2022
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...
Privacy Computer Security Internet of Things Deep Learning Information Storage and Retrieval
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Personalized On-Device E-Health Analytics With Decentralized Block Coordinate Descent.

IEEE journal of biomedical and health informatics Jun 3, 2022
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...
Humans Neural Networks, Computer Privacy Telemedicine Computer Simulation Machine Learning
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A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data.

Scientific reports May 25, 2022
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 ...
Machine Learning Learning Privacy Delivery of Health Care Data Collection
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Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.

Scientific reports May 19, 2022
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. ...
Privacy Algorithms Sepsis Delivery of Health Care Humans Machine Learning
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Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning.

Computational intelligence and neuroscience May 5, 2022
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...
Algorithms Humans Delivery of Health Care Machine Learning Privacy Social Networking
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Federated Learning in Medical Imaging: Part I: Toward Multicentral Health Care Ecosystems.

Journal of the American College of Radiology : JACR Apr 26, 2022
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...
Algorithms Privacy Machine Learning Ecosystem Delivery of Health Care
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DACFL: Dynamic Average Consensus-Based Federated Learning in Decentralized Sensors Network.

Sensors (Basel, Switzerland) Apr 26, 2022
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...
Privacy Machine Learning Communication Consensus Learning
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Federated Learning in Medical Imaging: Part II: Methods, Challenges, and Considerations.

Journal of the American College of Radiology : JACR Apr 25, 2022
Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging because of the...
Algorithms Privacy Machine Learning Neural Networks, Computer Humans
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Deep Federated Adaptation: An Adaptative Residential Load Forecasting Approach with Federated Learning.

Sensors (Basel, Switzerland) Apr 24, 2022
Residential-level short-term load forecasting (STLF) is significant for power system operation. Data-driven forecasting models, especially machine-learning-based models, are sensitive to the amount of data. However, privacy and security concerns rais...
Privacy Machine Learning
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Communication-efficient federated learning via knowledge distillation.

Nature communications Apr 19, 2022
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the ...
Data Compression Privacy Humans Communication Machine Learning
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