<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Journal Article</dc:product_type><dc:title>Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access</dc:title><dc:creator>Chang, Hao-Hsuan; Song, Yifei; Doan, Thinh T.; Liu, Lingjia</dc:creator><dc:corporate_author/><dc:editor/><dc:description/><dc:publisher/><dc:date>2023-01-10</dc:date><dc:nsf_par_id>10394408</dc:nsf_par_id><dc:journal_name>IEEE Transactions on Wireless Communications</dc:journal_name><dc:journal_volume/><dc:journal_issue/><dc:page_range_or_elocation>1 to 1</dc:page_range_or_elocation><dc:issn>1536-1276</dc:issn><dc:isbn/><dc:doi>https://doi.org/10.1109/TWC.2022.3233436</dc:doi><dcq:identifierAwardId>1811720; 1811497</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>