{"id":"https://openalex.org/W7127054749","doi":"https://doi.org/10.1145/3784833.3784863","title":"Efficient Federated Learning for 6G Network Based on MADRL and DTN","display_name":"Efficient Federated Learning for 6G Network Based on MADRL and DTN","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7127054749","doi":"https://doi.org/10.1145/3784833.3784863"},"language":null,"primary_location":{"id":"doi:10.1145/3784833.3784863","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3784833.3784863","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 11th International Conference on Communication and Information Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3784833.3784863","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124739420","display_name":"Huanran Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huanran Hu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0002-9504-6134","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124704200","display_name":"Yitong Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yitong Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1202-6174","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124725428","display_name":"Tianjiao Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianjiao Chen","raw_affiliation_strings":["Future Research Lab, China Mobile Research Institute, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2931-3487","affiliations":[{"raw_affiliation_string":"Future Research Lab, China Mobile Research Institute, Beijing, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124712608","display_name":"Baoyu Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoyu Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0003-9400-209X","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124726274","display_name":"Xinyao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyao Wang","raw_affiliation_strings":["Future Research Lab, China Mobile Research Institute, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5529-9554","affiliations":[{"raw_affiliation_string":"Future Research Lab, China Mobile Research Institute, Beijing, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124719963","display_name":"Yingping Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingping Cui","raw_affiliation_strings":["Future Research Lab, China Mobile Research Institute, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4342-6856","affiliations":[{"raw_affiliation_string":"Future Research Lab, China Mobile Research Institute, Beijing, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124710156","display_name":"Hongwen Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwen Yang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7541-8474","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6664045,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"405","last_page":"410"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.27559998631477356,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.27559998631477356,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11458","display_name":"Advanced Wireless Communication Technologies","score":0.12520000338554382,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13918","display_name":"Advanced Data and IoT Technologies","score":0.11969999969005585,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5263000130653381},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.5048999786376953},{"id":"https://openalex.org/keywords/telecommunications-link","display_name":"Telecommunications link","score":0.40459999442100525},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.3873000144958496},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.3675999939441681},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35530000925064087},{"id":"https://openalex.org/keywords/efficient-algorithm","display_name":"Efficient algorithm","score":0.3190000057220459},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.31850001215934753}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7940999865531921},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5263000130653381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5221999883651733},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.5048999786376953},{"id":"https://openalex.org/C138660444","wikidata":"https://www.wikidata.org/wiki/Q5607897","display_name":"Telecommunications link","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3873000144958496},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.38600000739097595},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3727000057697296},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35530000925064087},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.33469998836517334},{"id":"https://openalex.org/C3018263672","wikidata":"https://www.wikidata.org/wiki/Q1296251","display_name":"Efficient algorithm","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.31850001215934753},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C56951928","wikidata":"https://www.wikidata.org/wiki/Q3539213","display_name":"Trimming","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C203274722","wikidata":"https://www.wikidata.org/wiki/Q7001161","display_name":"Network performance","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C110994511","wikidata":"https://www.wikidata.org/wiki/Q661020","display_name":"Next-generation network","level":3,"score":0.272599995136261},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.26330000162124634},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.25519999861717224},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3784833.3784863","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3784833.3784863","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 11th International Conference on Communication and Information Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3784833.3784863","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3784833.3784863","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 11th International Conference on Communication and Information Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2126060647","https://openalex.org/W3014538993","https://openalex.org/W3045027907","https://openalex.org/W3045747588","https://openalex.org/W3109847748","https://openalex.org/W3118874133","https://openalex.org/W3163231210","https://openalex.org/W4206900626","https://openalex.org/W4281713353","https://openalex.org/W4376480773","https://openalex.org/W4380607150","https://openalex.org/W4387415360","https://openalex.org/W4392152744"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,27,40,47,56,75,123,140],"advancement":[2],"of":[3,29,42,49,58,78,131],"artificial":[4],"intelligence":[5],"(AI),":[6],"AI-native":[7],"networks":[8,23],"have":[9],"been":[10],"recognized":[11],"as":[12,39],"a":[13,155],"critical":[14],"direction":[15],"for":[16],"6G.":[17],"Federated":[18],"Learning":[19,109],"(FL)":[20],"in":[21,129],"6G":[22],"can":[24],"effectively":[25],"address":[26],"issue":[28],"non-independent":[30],"and":[31,46,82,111,134,138],"identically":[32],"distributed":[33],"(non-IID)":[34],"datasets.":[35],"However,":[36],"factors":[37],"such":[38],"heterogeneity":[41],"user":[43,80],"equipment":[44],"(UEs)":[45],"volatility":[48],"channel":[50],"conditions":[51],"pose":[52],"new":[53],"challenges":[54],"to":[55,73,89,149,153],"efficiency":[57,146],"FL.":[59],"The":[60],"Multi-Agent":[61,106],"Deep":[62,107],"Deterministic":[63],"Policy":[64],"Gradient":[65],"with":[66],"Optimized":[67],"States":[68],"(MADDPG-OS)":[69],"algorithm":[70,125],"is":[71,116],"proposed":[72,141],"optimize":[74],"pruning":[76],"rates":[77],"local":[79],"models":[81],"uplink":[83],"bandwidth":[84],"allocation.":[85],"This":[86],"approach":[87,103],"aims":[88],"minimize":[90],"FL":[91,102,144],"training":[92,96,136,145],"time":[93],"while":[94],"maintaining":[95],"performance.":[97],"In":[98],"addition,":[99],"an":[100],"efficient":[101],"based":[104],"on":[105],"Reinforcement":[108],"(MADRL)":[110],"Digital":[112],"Twin":[113],"Network":[114],"(DTN)":[115],"proposed.":[117],"Experimental":[118],"results":[119],"show":[120],"that":[121],"(1)":[122],"MADDPG-OS":[124],"outperforms":[126],"existing":[127],"algorithms":[128],"terms":[130],"convergence":[132],"performance":[133],"required":[135],"episodes,":[137],"(2)":[139],"method":[142],"improves":[143],"by":[147],"up":[148],"approximately":[150],"52.58%":[151],"compared":[152],"using":[154],"single":[156],"algorithm.":[157]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-02-03T00:00:00"}
