{"id":"https://openalex.org/W7162517522","doi":"https://doi.org/10.48550/arxiv.2605.26162","title":"On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach","display_name":"On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach","publication_year":2026,"publication_date":"2026-05-24","ids":{"openalex":"https://openalex.org/W7162517522","doi":"https://doi.org/10.48550/arxiv.2605.26162"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.26162","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26162","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.26162","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029267889","display_name":"Jiahui Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Jiahui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121059380","display_name":"Hai Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Hai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137185510","display_name":"A. K. Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, A. K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.5641999840736389,"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":0.5641999840736389,"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/T13553","display_name":"Age of Information Optimization","score":0.09650000184774399,"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"}},{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.03959999978542328,"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/asynchronous-communication","display_name":"Asynchronous communication","score":0.9415000081062317},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6959999799728394},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.6923999786376953},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5978000164031982},{"id":"https://openalex.org/keywords/data-aggregator","display_name":"Data aggregator","score":0.47929999232292175},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.46380001306533813},{"id":"https://openalex.org/keywords/data-exchange","display_name":"Data exchange","score":0.4593999981880188},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.39910000562667847}],"concepts":[{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.9415000081062317},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7940000295639038},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6959999799728394},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.6923999786376953},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5978000164031982},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5325000286102295},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.47929999232292175},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C15845906","wikidata":"https://www.wikidata.org/wiki/Q1172338","display_name":"Data exchange","level":2,"score":0.4593999981880188},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.39730000495910645},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C189693848","wikidata":"https://www.wikidata.org/wiki/Q6031064","display_name":"Information exchange","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28519999980926514},{"id":"https://openalex.org/C33762810","wikidata":"https://www.wikidata.org/wiki/Q461671","display_name":"Data integrity","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.271699994802475},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2705000042915344},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C2777072894","wikidata":"https://www.wikidata.org/wiki/Q4812204","display_name":"Asynchronous learning","level":5,"score":0.25839999318122864},{"id":"https://openalex.org/C2988382989","wikidata":"https://www.wikidata.org/wiki/Q370685","display_name":"Data space","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.26162","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26162","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.26162","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26162","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Asynchronous":[0],"decentralized":[1],"federated":[2],"learning":[3],"(ADFL)":[4],"eliminates":[5],"central":[6],"coordination":[7],"and":[8,16,28,39,57,65,80,94,111],"global":[9],"synchronization,":[10],"making":[11],"it":[12],"attractive":[13],"for":[14],"large-scale":[15],"heterogeneous":[17],"systems.":[18],"However,":[19],"frequent":[20],"peer-to-peer":[21],"communication,":[22,63],"asynchronous":[23,122],"updates":[24],"on":[25,125],"directed":[26],"topologies,":[27],"non-IID":[29],"data":[30,134],"jointly":[31],"lead":[32],"to":[33,90,106,138],"excessive":[34],"communication":[35,56,143],"overhead,":[36],"biased":[37],"aggregation":[38,92],"severe":[40],"model":[41],"drift.":[42],"We":[43],"propose":[44],"PushCen-ADFL,":[45],"a":[46,69,75,96,150],"communication-efficient":[47],"ADFL":[48],"framework":[49],"that":[50,129],"enables":[51],"stable":[52],"training":[53],"under":[54,109,120,133],"asymmetric":[55],"delayed":[58],"client":[59],"participation.":[60],"PushCen-ADFL":[61,130],"couples":[62],"aggregation,":[64],"local":[66],"stabilization":[67],"in":[68,101],"shared":[70],"centroid":[71,98,104],"representation":[72],"space,":[73],"forming":[74],"closed":[76],"loop":[77],"between":[78],"compression":[79],"optimization.":[81],"Clients":[82],"exchange":[83],"centroid-form":[84],"messages,":[85],"apply":[86],"average-preserving":[87],"push-sum":[88],"mixing":[89],"correct":[91],"bias,":[93],"use":[95],"lightweight":[97],"regularization":[99],"anchored":[100],"the":[102],"same":[103],"space":[105],"mitigate":[107],"drift":[108],"heterogeneity":[110,135],"staleness.":[112],"A":[113],"bounded,":[114],"sender-deduplicated":[115],"buffer":[116],"further":[117],"improves":[118,131],"robustness":[119],"irregular":[121],"arrivals.":[123],"Experiments":[124],"vision":[126],"datasets":[127],"demonstrate":[128],"accuracy":[132],"by":[136,145],"up":[137],"6\\%":[139],"while":[140],"reducing":[141],"per-push":[142],"cost":[144],"more":[146],"than":[147],"80\\%,":[148],"achieving":[149],"favorable":[151],"accuracy-communication":[152],"trade-off.":[153]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-28T00:00:00"}