{"id":"https://openalex.org/W4409983253","doi":"https://doi.org/10.1137/1.9781611978520.64","title":"FedGrAINS: Personalized SubGraph Federated Learning with AdaptIve Neighbor Sampling","display_name":"FedGrAINS: Personalized SubGraph Federated Learning with AdaptIve Neighbor Sampling","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409983253","doi":"https://doi.org/10.1137/1.9781611978520.64"},"language":"en","primary_location":{"id":"doi:10.1137/1.9781611978520.64","is_oa":false,"landing_page_url":"https://doi.org/10.1137/1.9781611978520.64","pdf_url":null,"source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 SIAM International Conference on Data Mining (SDM)","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.birmingham.ac.uk/en/publications/8040c9a4-0a15-472e-96af-8b242195a50e","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018617355","display_name":"Emir Ceyani","orcid":"https://orcid.org/0000-0002-4107-1337"},"institutions":[{"id":"https://openalex.org/I2800817003","display_name":"Southern California University for Professional Studies","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]},{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Emir Ceyani","raw_affiliation_strings":["University of Southern California"],"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007293006","display_name":"Han Xie","orcid":"https://orcid.org/0000-0001-7633-5040"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Han Xie","raw_affiliation_strings":["Emory University"],"affiliations":[{"raw_affiliation_string":"Emory University","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056993728","display_name":"Baturalp Buyukates","orcid":"https://orcid.org/0000-0002-5941-0667"},"institutions":[{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Baturalp Buyukates","raw_affiliation_strings":["University of Birmingham"],"affiliations":[{"raw_affiliation_string":"University of Birmingham","institution_ids":["https://openalex.org/I79619799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006897094","display_name":"Carl Yang","orcid":"https://orcid.org/0000-0001-9145-4531"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carl Yang","raw_affiliation_strings":["Emory University"],"affiliations":[{"raw_affiliation_string":"Emory University","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112726818","display_name":"Salman Avestimehr","orcid":null},"institutions":[{"id":"https://openalex.org/I2800817003","display_name":"Southern California University for Professional Studies","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]},{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Salman Avestimehr","raw_affiliation_strings":["University of Southern California"],"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5018617355"],"corresponding_institution_ids":["https://openalex.org/I1174212","https://openalex.org/I2800817003"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16576614,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"598","last_page":"607"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9941999912261963,"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":0.9941999912261963,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9772999882698059,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9531999826431274,"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/computer-science","display_name":"Computer science","score":0.5342879295349121}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5342879295349121}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1137/1.9781611978520.64","is_oa":false,"landing_page_url":"https://doi.org/10.1137/1.9781611978520.64","pdf_url":null,"source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 SIAM International Conference on Data Mining (SDM)","raw_type":"book-chapter"},{"id":"pmh:oai:pure.atira.dk:openaire/8040c9a4-0a15-472e-96af-8b242195a50e","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/8040c9a4-0a15-472e-96af-8b242195a50e","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"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":"Ceyani, E, Xie, H, Buyukates, B, Yang, C & Avestimehr, S 2025, FedGrAINS : Personalized SubGraph Federated Learning with AdaptIve Neighbor Sampling. in V Papalexakis, M Riondato, E Zheleva, T Weninger & W Ding (eds), Proceedings of the 2025 SIAM International Conference on Data Mining (SDM). Proceedings of the SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics (SIAM), pp. 598-607, 2025 SIAM International Conference on Data Mining, SDM 2025, Alexandria, United States, 1/05/25. https://doi.org/10.1137/1.9781611978520.64","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:openaire/8040c9a4-0a15-472e-96af-8b242195a50e","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/8040c9a4-0a15-472e-96af-8b242195a50e","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"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":"Ceyani, E, Xie, H, Buyukates, B, Yang, C & Avestimehr, S 2025, FedGrAINS : Personalized SubGraph Federated Learning with AdaptIve Neighbor Sampling. in V Papalexakis, M Riondato, E Zheleva, T Weninger & W Ding (eds), Proceedings of the 2025 SIAM International Conference on Data Mining (SDM). Proceedings of the SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics (SIAM), pp. 598-607, 2025 SIAM International Conference on Data Mining, SDM 2025, Alexandria, United States, 1/05/25. https://doi.org/10.1137/1.9781611978520.64","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Graphs":[0],"are":[1],"crucial":[2],"for":[3,52,118],"modeling":[4],"relational":[5],"and":[6,36,114],"biological":[7],"data.":[8],"As":[9],"datasets":[10],"grow":[11],"larger":[12],"in":[13,59,87,139],"real-world":[14],"scenarios,":[15],"the":[16,49,66,85,95,136,157,166],"risk":[17],"of":[18,101,159],"exposing":[19],"sensitive":[20],"information":[21],"increases,":[22],"making":[23],"privacy-preserving":[24],"training":[25,53,100],"methods":[26,46],"like":[27],"federated":[28,61,99],"learning":[29],"(FL)":[30],"essential":[31],"to":[32,73,84,127,170],"ensure":[33],"data":[34],"security":[35],"compliance":[37],"with":[38,65,148],"privacy":[39,74],"regulations.":[40],"Recently":[41],"proposed":[42],"personalized":[43,54,77],"subgraph":[44,78,119],"FL":[45,79,167],"have":[47],"become":[48],"de-facto":[50],"standard":[51],"Graph":[55],"Neural":[56],"Networks":[57],"(GNNs)":[58],"a":[60,111,149,162],"manner":[62],"while":[63],"dealing":[64],"missing":[67],"links":[68],"across":[69],"clients\u2019":[70,132,140],"subgraphs":[71],"due":[72,83],"restrictions.":[75],"However,":[76],"faces":[80],"significant":[81],"challenges":[82],"heterogeneity":[86],"client":[88],"subgraphs,":[89],"such":[90,176],"as":[91,161],"degree":[92],"distributions":[93],"among":[94],"nodes,":[96],"which":[97],"complicate":[98],"graph":[102],"models.":[103],"To":[104],"address":[105],"these":[106],"challenges,":[107],"we":[108],"propose":[109],"FedGrAINS,":[110],"novel":[112],"data-adaptive":[113],"sampling-based":[115],"regularization":[116],"method":[117],"FL.":[120],"FedGrAINS":[121,160],"leverages":[122],"generative":[123],"flow":[124],"networks":[125],"(GFlowNets)":[126],"evaluate":[128],"node":[129],"importance":[130],"concerning":[131],"tasks,":[133],"dynamically":[134],"adjusting":[135],"message-passing":[137],"step":[138],"GNNs.":[141],"This":[142],"adaptation":[143],"reflects":[144],"task-optimized":[145],"sampling":[146],"aligned":[147],"trajectory":[150],"balance":[151],"objective.":[152],"Experimental":[153],"results":[154],"demonstrate":[155],"that":[156,172],"inclusion":[158],"regularizer":[163],"consistently":[164],"improves":[165],"performance":[168],"compared":[169],"baselines":[171],"do":[173],"not":[174],"leverage":[175],"regularization.":[177]},"counts_by_year":[],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2025-10-10T00:00:00"}
