Apheris's ADMET Network will combine proprietary datasets in a privacy-preserving environment to accelerate AI drug discovery models.

The next generation of financial crime prevention will be built on smarter architectures, not bigger data pools.

The history of AI training has been shaped by the limits of communication. For years, progress depended on placing machines ever closer together, inside increasingly complex data centers.

Abstract: In this paper, we study the decentralized federated learning problem, which involves the collaborative training of a global model among multiple devices while ensuring data privacy. In ...

This GitHub repository contains the code, data, and figures for the paper FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models. Also includes ...

It supports client-wise data partitioning and federated learning with feature selection for high-dimensional tabular datasets like IoT-IDS or spam classification. spambase-fed-bfa.ipynb Federated BFA ...

Vikram Gupta is Chief Product Officer, SVP & GM of the IoT Processor Business Division at Synaptics, a leading EdgeAI semiconductor company. In my previous articles, I explored how the rapid growth of ...

When it comes to teaching math, a debate has persisted for decades: How, and to what degree, should algorithms be a focus of learning math? The step-by-step procedures are among the most debated ...

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 ...

Researchers have successfully employed an algorithm to identify potential mutations which increase disease risk in the noncoding regions our DNA, which make up the vast majority of the human genome.