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Energy-Efficient and Privacy-Preserving Intrusion Detection in Edge-Based Networks Using Federated Self-Supervised Learning

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Bibliographic Details
Published in:International Journal of Wireless and Microwave Technologies
Format: Online Article RSS Article
Published: 2026
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container_title International Journal of Wireless and Microwave Technologies
description
discipline_display Internet
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format Online Article
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genre Journal Article
id rss_article:67211
institution FRELIP
journal_source_facet International Journal of Wireless and Microwave Technologies
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Energy-Efficient and Privacy-Preserving Intrusion Detection in Edge-Based Networks Using Federated Self-Supervised Learning
Internet
General
Internet
sub_discipline_display General
sub_discipline_facet General
subject_display Internet
General
Internet
Internet
General
Internet
subject_facet Internet
General
Internet
title Energy-Efficient and Privacy-Preserving Intrusion Detection in Edge-Based Networks Using Federated Self-Supervised Learning
title_auth Energy-Efficient and Privacy-Preserving Intrusion Detection in Edge-Based Networks Using Federated Self-Supervised Learning
title_full Energy-Efficient and Privacy-Preserving Intrusion Detection in Edge-Based Networks Using Federated Self-Supervised Learning
title_fullStr Energy-Efficient and Privacy-Preserving Intrusion Detection in Edge-Based Networks Using Federated Self-Supervised Learning
title_full_unstemmed Energy-Efficient and Privacy-Preserving Intrusion Detection in Edge-Based Networks Using Federated Self-Supervised Learning
title_short Energy-Efficient and Privacy-Preserving Intrusion Detection in Edge-Based Networks Using Federated Self-Supervised Learning
title_sort energy-efficient and privacy-preserving intrusion detection in edge-based networks using federated self-supervised learning
topic Internet
General
Internet
url https://www.mecs-press.org/ijwmt/ijwmt-v16-n3/v16n3-17.html