<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>deep learning on Chiara Camerota</title><link>https://camerotachiara.github.io/tags/deep-learning/</link><description>Recent content in deep learning on Chiara Camerota</description><generator>Hugo -- 0.114.0</generator><language>en</language><atom:link href="https://camerotachiara.github.io/tags/deep-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Addressing Data Security in IoT: Minimum Sample Size and Denoising Diffusion Models for Improved Malware Detection</title><link>https://camerotachiara.github.io/papers/paper1/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://camerotachiara.github.io/papers/paper1/</guid><description>This paper investigates on how to define the miimum sample siz and improving the data agumentation for Traffic Images. Published 20th International Conference on Network and Service Management, 2024.</description></item><item><title>Taming Bandwidth Bottlenecks in Federated Learning via ECN-based Gradient Compression</title><link>https://camerotachiara.github.io/papers/paper_cnsm/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://camerotachiara.github.io/papers/paper_cnsm/</guid><description>The paper addresses communication bottlenecks in Federated Learning and introduces Selective Compression via Adaptive Lightweight Protocol, which dynamically selects compression levels based on local gradient variance and uplink bandwidth. Published on 2025 21st International Conference on Network and Service Management (CNSM).</description></item></channel></rss>