{"id":"https://openalex.org/W4415480952","doi":"https://doi.org/10.1145/3704413.3764433","title":"Lightweight Decentralized Federated Learning with Arbitrary Client Participation","display_name":"Lightweight Decentralized Federated Learning with Arbitrary Client Participation","publication_year":2025,"publication_date":"2025-10-23","ids":{"openalex":"https://openalex.org/W4415480952","doi":"https://doi.org/10.1145/3704413.3764433"},"language":null,"primary_location":{"id":"doi:10.1145/3704413.3764433","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3704413.3764433","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3704413.3764433","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 Twenty-sixth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3704413.3764433","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120110919","display_name":"Xinghan Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinghan Gong","raw_affiliation_strings":["Auburn University, Auburn, USA"],"raw_orcid":"https://orcid.org/0009-0004-9005-181X","affiliations":[{"raw_affiliation_string":"Auburn University, Auburn, USA","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042081570","display_name":"Xiaowen Gong","orcid":"https://orcid.org/0000-0001-5124-7941"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaowen Gong","raw_affiliation_strings":["Auburn University, Auburn, USA"],"raw_orcid":"https://orcid.org/0000-0001-5124-7941","affiliations":[{"raw_affiliation_string":"Auburn University, Auburn, USA","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100672752","display_name":"Ying Sun","orcid":"https://orcid.org/0000-0002-9709-6509"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Sun","raw_affiliation_strings":["The Pennsylvania State University, State College, USA"],"raw_orcid":"https://orcid.org/0000-0002-9709-6509","affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, State College, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080122431","display_name":"Shiwen Mao","orcid":"https://orcid.org/0000-0002-7052-0007"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shiwen Mao","raw_affiliation_strings":["Auburn University, Auburn, USA"],"raw_orcid":"https://orcid.org/0000-0002-7052-0007","affiliations":[{"raw_affiliation_string":"Auburn University, Auburn, USA","institution_ids":["https://openalex.org/I82497590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"161","last_page":"170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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/T10964","display_name":"Wireless Communication Security Techniques","score":0.996999979019165,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9966999888420105,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.8424000144004822},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6492999792098999},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.5424000024795532},{"id":"https://openalex.org/keywords/decentralised-system","display_name":"Decentralised system","score":0.5134000182151794},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4684999883174896},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.3521000146865845},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.32030001282691956}],"concepts":[{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.8424000144004822},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7731000185012817},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6492999792098999},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.5424000024795532},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.529699981212616},{"id":"https://openalex.org/C205875254","wikidata":"https://www.wikidata.org/wiki/Q17156857","display_name":"Decentralised system","level":3,"score":0.5134000182151794},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4684999883174896},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.32030001282691956},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.28949999809265137},{"id":"https://openalex.org/C2983222225","wikidata":"https://www.wikidata.org/wiki/Q2994424","display_name":"Consensus algorithm","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C2780821482","wikidata":"https://www.wikidata.org/wiki/Q25381721","display_name":"Crowdsensing","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C152880691","wikidata":"https://www.wikidata.org/wiki/Q146813","display_name":"Client\u2013server model","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3704413.3764433","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3704413.3764433","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3704413.3764433","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 Twenty-sixth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3704413.3764433","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3704413.3764433","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3704413.3764433","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 Twenty-sixth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1408516781","display_name":null,"funder_award_id":"CNS-2145031","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1722966035","display_name":"Collaborative Research: NewSpectrum: Toward Untethered Extended Reality Through Wireless Sensing and Communications Co-design","funder_award_id":"2434053","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2799665554","display_name":"Collaborative Research: NSF-MeitY: CNS Core: Small: Learning-Assisted Integrated Sensing, Communication and Security for 6G UAV Networks","funder_award_id":"2415208","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2939208937","display_name":"CAREER: Towards Efficient and Fast Hierarchical Federated Learning in Heterogeneous Wireless Edge Networks","funder_award_id":"2145031","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4746578975","display_name":null,"funder_award_id":"CCSS-2434053","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5927015223","display_name":null,"funder_award_id":"CNS-2415208","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7672805166","display_name":"Collaborative Research: IMR: MM-1A: Functional Data Analysis-aided Learning Methods for Robust Wireless Measurements","funder_award_id":"2319342","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G875478738","display_name":null,"funder_award_id":"CNS-2319342","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415480952.pdf","grobid_xml":"https://content.openalex.org/works/W4415480952.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2116673134","https://openalex.org/W2137435346","https://openalex.org/W2737743075","https://openalex.org/W2962771678","https://openalex.org/W2967314080","https://openalex.org/W2975090317","https://openalex.org/W3152624284","https://openalex.org/W4226161382","https://openalex.org/W4226183928","https://openalex.org/W4313031434","https://openalex.org/W4386243289","https://openalex.org/W4386245173","https://openalex.org/W4403017778"],"related_works":[],"abstract_inverted_index":{"Decentralized":[0,152],"federated":[1,19,72],"learning":[2,20,73],"(DFL)":[3],"can":[4,54],"greatly":[5],"reduce":[6],"communication":[7,13],"costs":[8],"due":[9,113],"to":[10,16,58,114,205],"its":[11],"decentralized":[12,71],"structure":[14],"compared":[15],"traditional":[17],"centralized":[18,63],"(FL).":[21],"Existing":[22],"works":[23],"on":[24,62],"FL":[25],"with":[26,42,95],"partial":[27,115],"client":[28,97,138,160,167],"participation":[29,98,116,139,161],"often":[30],"considered":[31],"idealized":[32],"scenarios":[33],"(such":[34],"as":[35],"all":[36],"clients":[37,209],"participate":[38],"in":[39],"a":[40,86,121,129,141,184,194],"round":[41],"the":[43,100,124,135,169,228,233],"same":[44,170],"probability),":[45],"or":[46,60],"required":[47],"using":[48,219],"clients'":[49,109],"past":[50,80],"gradient/model":[51,81],"information":[52],"which":[53,156],"be":[55],"too":[56],"costly":[57],"implement,":[59],"focused":[61],"FL.":[64],"In":[65],"this":[66,147],"paper,":[67],"we":[68,149],"study":[69],"lightweight":[70,93],"that":[74,165,181,187,198],"does":[75],"not":[76],"use":[77],"any":[78],"client's":[79],"information.":[82],"We":[83,191],"first":[84],"present":[85],"novel":[87],"sample-path-based":[88],"cyclic":[89,105,125,159,195,235],"convergence":[90,106,185],"analysis":[91,107],"for":[92,99],"DFL":[94],"arbitrary":[96],"non-convex":[101],"objectives":[102],"case.":[103],"The":[104],"bounds":[108],"local":[110,174,215],"model":[111],"drifts":[112],"over":[117],"multiple":[118],"rounds":[119],"within":[120],"cycle":[122],"and":[123,202,210,226,232],"consensus":[126],"error":[127],"via":[128],"per-cycle":[130],"descent":[131],"approach,":[132],"while":[133],"capturing":[134],"effect":[136],"of":[137,173,214,230],"through":[140],"single":[142],"unified":[143],"term.":[144],"By":[145],"analyzing":[146],"term,":[148],"propose":[150,193],"Cyclic":[151],"Federated":[153],"Learning":[154],"(CDFL),":[155],"enables":[157],"general":[158],"by":[162],"requiring":[163],"only":[164],"each":[166],"performs":[168],"total":[171],"number":[172,213],"updates":[175],"per":[176],"cycle.":[177],"Our":[178],"results":[179,225],"show":[180],"CDFL":[182,231],"achieves":[183],"rate":[186],"matches":[188],"existing":[189],"benchmarks.":[190],"further":[192],"control":[196,236],"framework":[197],"is":[199],"both":[200],"training-round":[201],"energy":[203],"efficient":[204],"adaptively":[206],"select":[207],"participating":[208],"determine":[211],"their":[212],"updates.":[216],"Numerical":[217],"experiments":[218],"real-world":[220],"datasets":[221],"verify":[222],"our":[223],"theoretical":[224],"demonstrate":[227],"effectiveness":[229],"adaptive":[234],"framework.":[237]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-23T00:00:00"}
