{"id":"https://openalex.org/W7124139538","doi":"https://doi.org/10.1109/icpads67057.2025.11323180","title":"Client Selection for Multi-Task Federated Learning: A Lyapunov Optimization Approach","display_name":"Client Selection for Multi-Task Federated Learning: A Lyapunov Optimization Approach","publication_year":2025,"publication_date":"2025-12-14","ids":{"openalex":"https://openalex.org/W7124139538","doi":"https://doi.org/10.1109/icpads67057.2025.11323180"},"language":null,"primary_location":{"id":"doi:10.1109/icpads67057.2025.11323180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpads67057.2025.11323180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)","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":"https://openalex.org/A5123049549","display_name":"Jingzhou Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingzhou Wang","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122997133","display_name":"Xiumin Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiumin Wang","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007440245","display_name":"Weiwei Lin","orcid":"https://orcid.org/0000-0001-6876-1795"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Lin","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123012647","display_name":"Kai Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Liu","raw_affiliation_strings":["College of Computer Science, Chongqing University,Chongqing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Chongqing University,Chongqing,China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123019336","display_name":"Weiwei Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Wu","raw_affiliation_strings":["School of Computer Science and Engineering, Southeast University,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Southeast University,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9146000146865845,"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":0.9146000146865845,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.007799999788403511,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T13553","display_name":"Age of Information Optimization","score":0.006899999920278788,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.6762999892234802},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5759999752044678},{"id":"https://openalex.org/keywords/lyapunov-optimization","display_name":"Lyapunov optimization","score":0.5461000204086304},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5450000166893005},{"id":"https://openalex.org/keywords/cognitive-reframing","display_name":"Cognitive reframing","score":0.517300009727478},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5098999738693237},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.47850000858306885},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4165000021457672},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.41339999437332153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7531999945640564},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.6762999892234802},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5759999752044678},{"id":"https://openalex.org/C101403955","wikidata":"https://www.wikidata.org/wiki/Q6707083","display_name":"Lyapunov optimization","level":5,"score":0.5461000204086304},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5450000166893005},{"id":"https://openalex.org/C187029079","wikidata":"https://www.wikidata.org/wiki/Q958679","display_name":"Cognitive reframing","level":2,"score":0.517300009727478},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5098999738693237},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5080000162124634},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.47850000858306885},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4165000021457672},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.41339999437332153},{"id":"https://openalex.org/C60640748","wikidata":"https://www.wikidata.org/wiki/Q2337858","display_name":"Lyapunov function","level":3,"score":0.4097000062465668},{"id":"https://openalex.org/C160403385","wikidata":"https://www.wikidata.org/wiki/Q220543","display_name":"Queue","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C22684755","wikidata":"https://www.wikidata.org/wiki/Q847526","display_name":"Queueing theory","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C123197309","wikidata":"https://www.wikidata.org/wiki/Q2882343","display_name":"Multi-armed bandit","level":3,"score":0.34940001368522644},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.3183000087738037},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.29159998893737793},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28790000081062317},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26649999618530273},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpads67057.2025.11323180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpads67057.2025.11323180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4299995887","display_name":null,"funder_award_id":"2022B1515020015","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G5775599903","display_name":null,"funder_award_id":"62232004,62572120","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6562469463","display_name":null,"funder_award_id":"BK20230024","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2040263621","https://openalex.org/W2041670460","https://openalex.org/W2472333518","https://openalex.org/W2576683119","https://openalex.org/W2701059868","https://openalex.org/W2775440879","https://openalex.org/W2787118002","https://openalex.org/W2799040448","https://openalex.org/W3080934299","https://openalex.org/W3095593352","https://openalex.org/W3186051974","https://openalex.org/W3207378505","https://openalex.org/W4206220619","https://openalex.org/W4280617223","https://openalex.org/W4297911722","https://openalex.org/W4301852635","https://openalex.org/W4312906660","https://openalex.org/W4361854893","https://openalex.org/W4384705344","https://openalex.org/W4385893187","https://openalex.org/W4386902758","https://openalex.org/W4392909808","https://openalex.org/W4402156135","https://openalex.org/W4403759228","https://openalex.org/W4407467456","https://openalex.org/W4407900030","https://openalex.org/W7084046280"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"has":[3],"recently":[4],"garnered":[5],"considerable":[6],"attention":[7],"because":[8],"it":[9],"allows":[10],"multiple":[11],"clients":[12],"to":[13,97,141],"collaboratively":[14],"train":[15],"machine":[16],"learning":[17,67,103],"models":[18],"while":[19],"keeping":[20],"their":[21],"local":[22],"data":[23],"private.":[24],"However,":[25],"most":[26],"existing":[27],"studies":[28],"focus":[29],"primarily":[30],"on":[31,149],"optimizing":[32],"a":[33,56,85,91,121],"single":[34],"FL":[35,60,172],"task,":[36],"overlooking":[37],"the":[38,66,70,112,116,135,143,154,157],"dynamic":[39],"nature":[40],"of":[41,134,156],"systems":[42],"where":[43],"tasks":[44],"may":[45],"arrive":[46],"over":[47],"time.":[48],"To":[49,81],"address":[50],"this":[51,53],"issue,":[52],"paper":[54],"formulates":[55],"novel":[57],"long-term":[58],"multi-task":[59,171],"optimization":[61,93],"problem,":[62],"aiming":[63],"at":[64],"balancing":[65],"quality":[68],"and":[69,90,102,167],"penalties":[71],"incurred":[72],"from":[73],"insufficient":[74],"client":[75,117],"participation":[76],"in":[77,169],"each":[78],"communication":[79],"round.":[80],"mitigate":[82],"selection":[83,118],"bias,":[84],"fairness":[86,166],"queue":[87],"is":[88,95,138],"implemented,":[89],"Lyapunov":[92],"model":[94],"developed":[96],"enhance":[98],"both":[99,165],"system":[100],"stability":[101],"utility.":[104],"Furthermore,":[105],"we":[106],"derive":[107],"an":[108,128],"upper":[109],"bound":[110],"for":[111,163],"objective":[113],"function,":[114],"reframing":[115],"issue":[119],"as":[120],"minimum":[122],"weight":[123],"bipartite":[124,130],"matching":[125],"problem":[126],"within":[127],"auxiliary":[129],"graph.":[131],"The":[132],"regret":[133],"proposed":[136,158],"strategy":[137],"theoretically":[139],"analyzed":[140],"quantify":[142],"performance":[144],"gap.":[145],"Finally,":[146],"extensive":[147],"simulations":[148],"two":[150],"real":[151],"datasets":[152],"demonstrate":[153],"effectiveness":[155],"scheme,":[159],"highlighting":[160],"its":[161],"potential":[162],"improving":[164],"efficiency":[168],"dynamic,":[170],"environments.":[173]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-15T00:00:00"}