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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.

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) has gained significant attention as a novel distributed machine learning paradigm that enables collaborative model training while preserving data privacy. However, traditional ...

With the introduction of Google's Tensor Flow federated, the hype around federated machine learning is surging. But there are important questions about data privacy, performance and cost that need ...

In 2017, Google quietly published a blog post about a new approach to machine learning. Unlike the standard method, which requires the data to be centralized in one place, the new one could learn from ...

In talking with AI innovators who are tackling some of the most complex challenges in healthcare, three things are clear: At the heart of these challenges lie the issues of data sharing and patient ...

Privacy-preserving AI technique enables researchers to improve cancerous brain tumor detection by 33%. SANTA CLARA, Calif.--(BUSINESS WIRE)--What’s New: Intel Labs ...