A5066646595- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Stochastic Gradient Optimization Techniques
- Artificial Intelligence in Healthcare and Education
- Privacy, Security, and Data Protection
- Ethics in Clinical Research
- Radiomics and Machine Learning in Medical Imaging
- Smart Cities and Technologies
- COVID-19 diagnosis using AI
- Mobile Crowdsensing and Crowdsourcing
SBA Research
2020-2023
University of Southern Denmark
2023
Universität Hamburg
2023
Background Machine learning and artificial intelligence have shown promising results in many areas are driven by the increasing amount of available data. However, these data often distributed across different institutions cannot be easily shared owing to strict privacy regulations. Federated (FL) allows training machine models without sharing sensitive In addition, implementation is time-consuming requires advanced programming skills complex technical infrastructures. Objective Various tools...
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas are driven by the increasing amount of available data. However, this data is often distributed across different institutions cannot be shared due to privacy concerns. Privacy-preserving methods, such as Federated (FL), allow for training ML models without sharing sensitive data, but their implementation time-consuming requires advanced programming skills. Here, we present FeatureCloud AI Store...
Federated learning provides the solution when multiple parties want to collaboratively train a machine model without directly sharing sensitive data. In Learning, each party trains locally on its private data and sends only models' weights or updates (gradients) an aggregator, which averages trained models into new global with higher effectiveness. However, models, have be shared during federated process, can still leak information about their training through e.g. membership inference...
Federated Learning (FL) is a method that allows multiple entities to jointly train machine learning model using data located in various places. Unlike the conventional approach of gathering private from distributed locations central place, federated involves solely exchanging and aggregating models. Each party shares only trained locally on their data, ensuring sensitive remains within respective silos throughout process. However, these shared models FL may still leak information about...
<sec> <title>BACKGROUND</title> Machine learning and artificial intelligence have shown promising results in many areas are driven by the increasing amount of available data. However, these data often distributed across different institutions cannot be easily shared owing to strict privacy regulations. Federated (FL) allows training machine models without sharing sensitive In addition, implementation is time-consuming requires advanced programming skills complex technical infrastructures....