Industry 4.0 depends on continuous data exchange between sensors, machines, production lines, and enterprise systems, but much of this data cannot be centralized due to privacy, security, and ...

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 ...

Federated learning represents a paradigm shift in machine learning by enabling the collaborative training of models across multiple distributed nodes without requiring centralised data collection.

Digital personalization is demanded by customers in 2024, and going the extra mile for effective personalization is a key differentiating factor. In 2024, the demand for digital personalization ...

To get started: Data is the New Gold, but Privacy is the Vault! The data, and more specifically, customer data, is everything in finance. Whether you've seen it at a ...

The FecMap model trained in an iterative manner. An FL communication is completed by (1) training the local model, (2) uploading to the server, (3) computing the global model, and (4) updating the ...

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.

Researchers developed a system that streamlines the process of federated learning, a technique where users collaborate to train a machine-learning model in a way that safeguards each user's data. The ...