{"id":"https://openalex.org/W4366342364","doi":"https://doi.org/10.1145/3578356.3592579","title":"Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates","display_name":"Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates","publication_year":2023,"publication_date":"2023-05-04","ids":{"openalex":"https://openalex.org/W4366342364","doi":"https://doi.org/10.1145/3578356.3592579"},"language":"en","primary_location":{"id":"doi:10.1145/3578356.3592579","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3578356.3592579","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3578356.3592579","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 3rd Workshop on Machine Learning and Systems","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3578356.3592579","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107249455","display_name":"Chenyang Ma","orcid":"https://orcid.org/0009-0007-0737-3175"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Chenyang Ma","raw_affiliation_strings":["Cambridge Machine Learning Systems Lab (CaMLSys), University of Cambridge, Cambridge, Cambridge, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Cambridge Machine Learning Systems Lab (CaMLSys), University of Cambridge, Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040821950","display_name":"Xinchi Qiu","orcid":"https://orcid.org/0000-0002-7654-4448"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xinchi Qiu","raw_affiliation_strings":["Cambridge Machine Learning Systems Lab (CaMLSys), University of Cambridge, Cambridge, Cambridge, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Cambridge Machine Learning Systems Lab (CaMLSys), University of Cambridge, Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075204958","display_name":"Daniel J. Beutel","orcid":"https://orcid.org/0009-0008-6680-5959"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Daniel Beutel","raw_affiliation_strings":["Cambridge Machine Learning Systems Lab (CaMLSys), University of Cambridge, Cambridge, Cambridge, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Cambridge Machine Learning Systems Lab (CaMLSys), University of Cambridge, Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045638679","display_name":"Nicholas D. Lane","orcid":"https://orcid.org/0000-0002-2728-8273"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nicholas Lane","raw_affiliation_strings":["Cambridge Machine Learning Systems Lab (CaMLSys), University of Cambridge, Cambridge, Cambridge, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Cambridge Machine Learning Systems Lab (CaMLSys), University of Cambridge, Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5107249455"],"corresponding_institution_ids":["https://openalex.org/I241749"],"apc_list":null,"apc_paid":null,"fwci":3.4566,"has_fulltext":true,"cited_by_count":20,"citation_normalized_percentile":{"value":0.93846898,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"56","last_page":"63"},"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/T10237","display_name":"Cryptography and Data Security","score":0.9815000295639038,"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9592000246047974,"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/computer-science","display_name":"Computer science","score":0.7808235883712769},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6951454877853394},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.655684232711792},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6440796852111816},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5825923681259155},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5400354266166687},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5261630415916443},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5199914574623108},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5029634833335876},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.42260563373565674},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4104396402835846},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33828437328338623},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.14650768041610718},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09865742921829224},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08486276865005493}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7808235883712769},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6951454877853394},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.655684232711792},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6440796852111816},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5825923681259155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5400354266166687},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5261630415916443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5199914574623108},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5029634833335876},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.42260563373565674},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4104396402835846},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33828437328338623},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.14650768041610718},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09865742921829224},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08486276865005493},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3578356.3592579","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3578356.3592579","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3578356.3592579","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 3rd Workshop on Machine Learning and Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2304.07537","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2304.07537","pdf_url":"https://arxiv.org/pdf/2304.07537","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3578356.3592579","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3578356.3592579","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3578356.3592579","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 3rd Workshop on Machine Learning and Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4366342364.pdf","grobid_xml":"https://content.openalex.org/works/W4366342364.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W2247875277","https://openalex.org/W2295598076","https://openalex.org/W2997721570","https://openalex.org/W3164712068","https://openalex.org/W4226094645","https://openalex.org/W4247924304","https://openalex.org/W4249176245"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W3094138326"],"abstract_inverted_index":{"The":[0],"privacy-sensitive":[1],"nature":[2],"of":[3,9,26,43,75,89],"decentralized":[4],"datasets":[5],"and":[6,51,77,81,102,115,125],"the":[7,18,24,36,41,73,86,90,112],"robustness":[8],"eXtreme":[10],"Gradient":[11],"Boosting":[12],"(XGBoost)":[13],"on":[14,32,40,72,99],"tabular":[15],"data":[16],"raise":[17],"needs":[19],"to":[20,111,133],"train":[21],"XGBoost":[22,34,67],"in":[23,35],"context":[25],"federated":[27,33,66],"learning":[28,87],"(FL).":[29],"Existing":[30],"works":[31],"horizontal":[37,65],"setting":[38],"rely":[39],"sharing":[42,74],"gradients,":[44],"which":[45,68],"induce":[46],"per-node":[47],"level":[48],"communication":[49,82,118,123,126],"frequency":[50],"serious":[52],"privacy":[53,80],"concerns.":[54],"To":[55],"alleviate":[56],"these":[57],"problems,":[58],"we":[59],"develop":[60],"an":[61],"innovative":[62],"framework":[63],"for":[64],"does":[69],"not":[70],"depend":[71],"gradients":[76],"simultaneously":[78],"boosts":[79],"efficiency":[83,119],"by":[84,120,128],"making":[85],"rates":[88],"aggregated":[91],"tree":[92],"ensembles":[93],"learnable.":[94],"We":[95],"conduct":[96],"extensive":[97],"evaluations":[98],"various":[100],"classification":[101],"regression":[103],"datasets,":[104],"showing":[105],"our":[106],"approach":[107],"achieves":[108],"performance":[109],"comparable":[110],"state-of-the-art":[113],"method":[114],"effectively":[116],"improves":[117],"lowering":[121],"both":[122],"rounds":[124],"overhead":[127],"factors":[129],"ranging":[130],"from":[131],"25x":[132],"700x.":[134]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2023-04-20T00:00:00"}