Studying the Robustness of Anti-Adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors. | D2R Server publishing the DBLP Bibliography Database, hosted at L3S Research Center

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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Adrian_Lars_Benjamin_Iten>
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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Burkhard_Stiller>
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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Pedro_Miguel_S%E2%88%9A%C2%B0nchez_S%E2%88%9A%C2%B0nchez>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Timo_Schenk>
foaf:homepage	<http://dx.doi.org/doi.org%2F10.1109%2FTDSC.2022.3204535>
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dcterms:issued	2024 (xsd:gYear)
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swrc:month	March - April (xsd:string)
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dc:title	Studying the Robustness of Anti-Adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors. (xsd:string)
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