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Federated Learning in Medical Imaging: Part II: Methods, Challenges, and Considerations.

Journal of the American College of Radiology : JACR
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...

Deep Federated Adaptation: An Adaptative Residential Load Forecasting Approach with Federated Learning.

Sensors (Basel, Switzerland)
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...

Communication-efficient federated learning via knowledge distillation.

Nature communications
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 ...

Recommendation System for Privacy-Preserving Education Technologies.

Computational intelligence and neuroscience
Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the...

Personality Privacy Protection Method of Social Users Based on Generative Adversarial Networks.

Computational intelligence and neuroscience
Obscuring or otherwise minimizing the release of personality information from potential victims of social engineering attacks effectively interferes with an attacker's personality analysis and reduces the success rate of social engineering attacks. W...

DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Recent advances in next-generation sequencing technologies have led to the successful insertion of video information into DNA using synthesized oligonucleotides. Several attempts have been made to embed larger data into living organisms. This process...

Federated Deep Learning for the Diagnosis of Cerebellar Ataxia: Privacy Preservation and Auto-Crafted Feature Extractor.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been...

MFDroid: A Stacking Ensemble Learning Framework for Android Malware Detection.

Sensors (Basel, Switzerland)
As Android is a popular a mobile operating system, Android malware is on the rise, which poses a great threat to user privacy and security. Considering the poor detection effects of the single feature selection algorithm and the low detection efficie...

A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning.

Sensors (Basel, Switzerland)
As an emerging artificial intelligence technology, federated learning plays a significant role in privacy preservation in machine learning, although its main objective is to prevent peers from peeping data. However, attackers from the outside can ste...

Ethical Design and Use of Robotic Care of the Elderly.

Journal of bioethical inquiry
The Australian Royal Commission into Aged Care Quality and Safety acknowledged understaffing and substandard care in residential aged care and home care services, and recommendations were made that that the Australian Government should promote assist...