{"id":"https://openalex.org/W7084161475","doi":"https://doi.org/10.1109/infocom55648.2025.11044690","title":"Accelerating Clustered Federated Learning in Dynamic D2D Networks with Transferable GNN","display_name":"Accelerating Clustered Federated Learning in Dynamic D2D Networks with Transferable GNN","publication_year":2025,"publication_date":"2025-05-19","ids":{"openalex":"https://openalex.org/W7084161475","doi":"https://doi.org/10.1109/infocom55648.2025.11044690"},"language":"en","primary_location":{"id":"doi:10.1109/infocom55648.2025.11044690","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infocom55648.2025.11044690","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE INFOCOM 2025 - IEEE Conference on Computer Communications","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yuhong Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yuhong Jiang","raw_affiliation_strings":["University of Exeter,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Exeter,Department of Computer Science","institution_ids":["https://openalex.org/I23923803"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jia Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jia Hu","raw_affiliation_strings":["University of Exeter,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Exeter,Department of Computer Science","institution_ids":["https://openalex.org/I23923803"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I23923803"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.56185832,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11423","display_name":"Pesticide Residue Analysis and Safety","score":0.03519999980926514,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11423","display_name":"Pesticide Residue Analysis and Safety","score":0.03519999980926514,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11641","display_name":"Insect and Pesticide Research","score":0.02710000053048134,"subfield":{"id":"https://openalex.org/subfields/1109","display_name":"Insect Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13054","display_name":"Agricultural and Financial Auditing","score":0.024399999529123306,"subfield":{"id":"https://openalex.org/subfields/1100","display_name":"General Agricultural and Biological Sciences"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5751000046730042},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.5486999750137329},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5157999992370605},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.49239999055862427},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.45559999346733093},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.40849998593330383},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.3869999945163727},{"id":"https://openalex.org/keywords/dynamic-network-analysis","display_name":"Dynamic network analysis","score":0.373199999332428},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.33489999175071716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.820900022983551},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5751000046730042},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.572700023651123},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.5486999750137329},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.373199999332428},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3434999883174896},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3061999976634979},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.289900004863739},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2849000096321106},{"id":"https://openalex.org/C158207573","wikidata":"https://www.wikidata.org/wiki/Q5747224","display_name":"Heterogeneous network","level":4,"score":0.2838999927043915},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.2809999883174896},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.2556999921798706},{"id":"https://openalex.org/C189693848","wikidata":"https://www.wikidata.org/wiki/Q6031064","display_name":"Information exchange","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/infocom55648.2025.11044690","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infocom55648.2025.11044690","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE INFOCOM 2025 - IEEE Conference on Computer Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Heterogeneous":[0],"computation":[1],"and":[2,69,77,109,131,174,177,190],"communication":[3,192],"resources":[4,78],"across":[5],"mobile":[6],"devices":[7,53,149],"drastically":[8],"degrade":[9],"the":[10,74,126,160,178],"performance":[11],"of":[12,128],"Federated":[13],"Learning":[14],"(FL),":[15],"while":[16],"clustered":[17,29],"FL":[18,30,95,99,185],"is":[19],"recognized":[20],"as":[21,105],"an":[22],"effective":[23],"solution":[24],"to":[25,73,118,124,197],"this":[26,121],"issue.":[27],"Traditional":[28],"methods":[31,64],"rely":[32],"on":[33],"a":[34,43,88,106,111,136,156],"cluster":[35,44],"head":[36,45],"for":[37,146,159],"intra-cluster":[38],"model":[39,114],"aggregation,":[40],"however,":[41],"such":[42],"that":[46,181],"can":[47],"directly":[48],"communicate":[49],"with":[50,171],"all":[51],"other":[52],"may":[54],"not":[55],"exist":[56],"in":[57,79,101],"practical":[58],"Device-to-Device":[59],"(D2D)":[60],"networks.":[61,81],"Besides,":[62],"most":[63],"consider":[65],"static":[66],"network":[67,173],"conditions":[68],"thus":[70],"cannot":[71],"adapt":[72],"dynamic":[75,102,139],"topologies":[76],"D2D":[80,103,137],"To":[82],"address":[83],"these":[84],"challenges,":[85],"we":[86,134,167],"propose":[87],"Transferable":[89],"Graph":[90],"Neural":[91],"Network":[92],"(GNN)-based":[93],"Clustered":[94],"method,":[96],"which":[97],"formulates":[98],"clustering":[100],"networks":[104],"graph":[107],"problem":[108],"develops":[110],"transferable":[112],"GNN":[113],"using":[115],"unsupervised":[116],"training":[117],"adaptively":[119],"solve":[120],"problem.":[122],"Furthermore,":[123],"alleviate":[125],"impact":[127],"data":[129,175],"heterogeneity":[130],"accelerate":[132],"FL,":[133],"design":[135],"connectivity-aware":[138],"programming":[140],"algorithm":[141],"driven":[142],"by":[143,188,194],"Mutual":[144],"Information":[145],"selecting":[147],"participating":[148],"within":[150],"each":[151],"cluster.":[152],"We":[153],"also":[154],"provide":[155],"convergence":[157],"bound":[158],"global":[161],"loss":[162],"through":[163],"theoretical":[164],"analysis.":[165],"Finally,":[166],"conduct":[168],"extensive":[169],"experiments":[170],"various":[172],"settings,":[176],"results":[179],"demonstrate":[180],"our":[182],"method":[183],"improves":[184],"time":[186],"efficiency":[187],"24%-78%":[189],"reduces":[191],"cost":[193],"30%-88%":[195],"compared":[196],"key":[198],"baselines.":[199]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
