A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted consumer data. By combining secure clustering with efficient computation, the study ...
Atharv Kolhar, a staff test automation engineer at Figure AI, says the robotics industry needs a testing philosophy that scales alongside autonomy.
As patients are divided into ever more narrowly defined subgroups, the number of individuals available for research shrinks dramatically. While this approach improves personalization, it also creates ...
AI medical imaging market is projected to exceed $20B by 2035. Generative models address class imbalances in medical imaging ...
ABSTRACT: Bipolar disorder (BD) affects approximately 45 million individuals worldwide and is characterized by recurrent episodes of mania, hypomania, and depression, with an average diagnostic delay ...
The rapid expansion of Internet of Things (IoT) devices in smart healthcare systems has led to the generation of large volumes of diverse medical data. This creates challenges in ensuring secure, ...
Federated Learning (FL) is a distributed Machine Learning (ML) paradigm that enables multiple local devices, that is, clients, and a central server to collaboratively train a ML model using data ...
Existing supply chain information security methods all suffer from the difficulty of balancing information sharing efficiency and information privacy protection. Data is prone to leakage, resulting in ...
If it feels like social platforms suddenly “get” you more than they used to, you’re not imagining it! In 2026, feeds aren’t only reacting to what you click anymore. They’re predicting what you ...
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 ...
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 ...
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 ...
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