The Hong Kong Applied Science and Technology Research Institute (ASTRI) joins forces with tech-embracing companies to leverage a privacy-preserving technology, called “Federated Learning”, to develop ...

Data privacy regulations like GDPR, the CCPA and HIPAA present a challenge to training AI systems on sensitive data, like financial transactions, patient health records and user device logs.

In a recent study published in The Lancet Digital Health, a group of researchers developed and evaluated a scalable, privacy-preserving federated learning solution using low-cost microcomputing for ...

Applying decentralized learning Investigating training algorithms for private federated learning, combining causal and federated learning, using federated reinforcement learning principles, federated ...

Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, ...

Federated Learning (FL) allows for privacy-preserving model training by enabling clients to upload model gradients without exposing their personal data. However, the decentralized nature of FL ...

Artificial intelligence (AI) and machine learning (ML) have the power to deliver business value and impact across a wide range of use cases, which has led to their rapidly increasing deployment across ...

In machine learning, privacy risks often emerge from inference-based attacks. Model inversion techniques can reconstruct ...