In this tutorial, we demonstrate how we simulate a privacy-preserving fraud detection system using Federated Learning without relying on heavyweight frameworks or complex infrastructure. We build a ...
Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset
Abstract: Federated Learning is a distributed machine learning paradigm that enables model training across decentralized devices holding local data, thereby preserving data privacy and reducing the ...
Sandia National Laboratories released information today spotlighting what the labs call a significant milestone in advancing artificial intelligence for national security. Over the past year, Sandia, ...
At this year’s Credit Scoring and Credit Control Conference in Edinburgh, colleagues Ben Archer and Peter Szocs presented on a topic gaining significant attention: how federated learning can support ...
Abbas Yazdinejad does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond ...
As the use of Unmanned Aerial Vehicles (UAVs) expands across various fields, there is growing interest in leveraging Federated Learning (FL) to enhance the efficiency of UAV networks. However, ...
The Great Power Competition is no longer confined to traditional warfare. It plays out in data, algorithms and artificial intelligence (AI). As adversaries weaponize misinformation and cyber attacks ...
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