Federated Learning Intrusion Detection System using DNN(MLP) models

<!-- SC_OFF --><div class="md"><p>I'm a CS/ML engineering student in my 4th year, and I need help for a project I recently got assigned to (as an &quot;end of the year&quot;

<!-- SC_OFF --><div class="md"><p><a href="https://www.reddit.com/r/MachineLearning/?f=flair_name%3A%22Project%22"></a>Hey everyone. I’ve been working on a machine learning

<p>Anthropic just dropped <a href="https://www.anthropic.com/glasswing" rel="noopener noreferrer">Project Glasswing</a> — a big collaborative cybersecurity initiative with a shiny

<!-- SC_OFF --><div class="md"><p>Lately, I was creating content about <strong>Support Vector Machines (SVM)</strong>. The reason it was on my radar is that I saw a recent Kaggle

<!-- SC_OFF --><div class="md"><p>Just a Student trying to build some projects and this one caught my attention.</p> <p>An automated system for detecting statistically significant

<!-- SC_OFF --><div class="md"><p>The BDH (Dragon Hatchling) paper (arXiv:2509.26507) describes a Hebbian synaptic plasticity mechanism where model weights update during

<!-- SC_OFF --><div class="md"><p>Just putting together a quick list of the new <strong>open-source physical AI / robotics models</strong> from <strong>NVIDIA GTC

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s59rct/rys_part_3_llms_think_in_geometry_not_language/"> <img alt="RYS Part 3: LLMs think in geometry, not