<?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>Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission</dc:title><dc:creator>Chen, Rui; Li, Liang; Xue, Kaiping; Zhang, Chi; Pan, Miao; Fang, Yuguang</dc:creator><dc:corporate_author/><dc:editor/><dc:description/><dc:publisher>IEEE</dc:publisher><dc:date>2022-10-11</dc:date><dc:nsf_par_id>10441051</dc:nsf_par_id><dc:journal_name>IEEE Transactions on Mobile Computing</dc:journal_name><dc:journal_volume>22</dc:journal_volume><dc:journal_issue>12</dc:journal_issue><dc:page_range_or_elocation>1 to 13</dc:page_range_or_elocation><dc:issn>1536-1233</dc:issn><dc:isbn/><dc:doi>https://doi.org/10.1109/TMC.2022.3213766</dc:doi><dcq:identifierAwardId>2107057; 2106589</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>