A collaborative approach to training AI models can yield better results, but it requires finding partners with data that complements your own.

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.

After two and a half years of work, the MLEDGE project (Cloud and Edge Machine Learning), led by Professor Nikolaos Laoutaris at IMDEA Networks, ...

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 ...

Ed Hicks, business development manager for federal and artificial intelligence at Dell Technologies (NYSE: DELL), said government agencies that intend to implement AI at the edge should consider ...

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 ...

Federated learning makes it possible for agency employees to collaborate on advanced artificial intelligence models without compromising data control or operational security. The process serves as a ...

Let’s imagine a fictional company, Global Retail Corporation, a multinational retail chain struggling with its initial approach to AI integration. They built custom generative AI applications on their ...

FedCare delivers the first visual pipeline that pinpoints, classifies and mitigates FL failures in real time, cutting ...

Researchers have successfully developed the technology that can accurately segment different body organs by effectively learning medical image data used for different purposes in different hospitals, ...