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 ...
ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...
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 ...
The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...
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 ...
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 ...
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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