{"id":"https://openalex.org/W4310427480","doi":"https://doi.org/10.48550/arxiv.2211.15281","title":"Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing","display_name":"Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing","publication_year":2022,"publication_date":"2022-11-28","ids":{"openalex":"https://openalex.org/W4310427480","doi":"https://doi.org/10.48550/arxiv.2211.15281"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2211.15281","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.15281","pdf_url":"https://arxiv.org/pdf/2211.15281","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2211.15281","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110927950","display_name":"Kunjal Panchal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Panchal, Kunjal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064726544","display_name":"Sunav Choudhary","orcid":"https://orcid.org/0000-0002-7711-487X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choudhary, Sunav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Parikh, Nisarg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Parikh, Nisarg","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, Lijun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lijun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Guan, Hui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guan, Hui","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":true,"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":0.9955999851226807,"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":0.9955999851226807,"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":0.9926000237464905,"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/T11478","display_name":"Caching and Content Delivery","score":0.9643999934196472,"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/personalization","display_name":"Personalization","score":0.8652624487876892},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.80652916431427},{"id":"https://openalex.org/keywords/stateless-protocol","display_name":"Stateless protocol","score":0.7768521308898926},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.6596854329109192},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.5205210447311401},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.4822169542312622},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3970579504966736},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3943052887916565},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35287201404571533},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.3304075002670288},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.28379303216934204},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.19873684644699097},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10217925906181335}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.8652624487876892},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.80652916431427},{"id":"https://openalex.org/C103613024","wikidata":"https://www.wikidata.org/wiki/Q230924","display_name":"Stateless protocol","level":3,"score":0.7768521308898926},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.6596854329109192},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5205210447311401},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.4822169542312622},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3970579504966736},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3943052887916565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35287201404571533},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3304075002670288},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.28379303216934204},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.19873684644699097},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10217925906181335},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2211.15281","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.15281","pdf_url":"https://arxiv.org/pdf/2211.15281","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2211.15281","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2211.15281","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"pmh:oai:arXiv.org:2211.15281","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.15281","pdf_url":"https://arxiv.org/pdf/2211.15281","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.5,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[{"id":"https://openalex.org/G1957757468","display_name":"Collaborative Research: CSR: Medium: MemDrive: Memory-Driven Full-Stack Collaboration for Autonomous Embedded Systems","funder_award_id":"2312396","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2184662362","display_name":"ATD: Deep Learning on Anomaly Detection for Human Dynamics and Hazard Response","funder_award_id":"2220211","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7264624474","display_name":"CNS Core: Small: Transparently Scaling Graph Neural Network Training to Large-Scale Models and Graphs","funder_award_id":"2224054","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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3040374273","https://openalex.org/W2955572513","https://openalex.org/W4360604845","https://openalex.org/W151293476","https://openalex.org/W2931688134","https://openalex.org/W2116712504","https://openalex.org/W2036359834","https://openalex.org/W3130948357","https://openalex.org/W2377919138","https://openalex.org/W2002863235"],"abstract_inverted_index":{"Personalization":[0],"in":[1,21,103,158],"Federated":[2],"Learning":[3],"(FL)":[4],"aims":[5],"to":[6,14,19,57,105,109,126,144,171],"modify":[7],"a":[8,25,34,68,81,124],"collaboratively":[9],"trained":[10],"global":[11,54,96],"model":[12,55],"according":[13],"each":[15,113],"client.":[16,114],"Current":[17],"approaches":[18,170],"personalization":[20,108],"FL":[22,72,132,140],"are":[23,48],"at":[24,112],"coarse":[26],"granularity,":[27],"i.e.":[28],"all":[29],"the":[30,37,43,53,91,156],"input":[31,88],"instances":[32,47],"of":[33,161],"client":[35,125],"use":[36],"same":[38],"personalized":[39,71,77,129,169],"model.":[40],"This":[41,134],"ignores":[42],"fact":[44],"that":[45,84],"some":[46],"more":[49],"accurately":[50],"handled":[51],"by":[52,79],"due":[56],"better":[58],"generalizability.":[59],"To":[60],"address":[61],"this":[62,64],"challenge,":[63],"work":[65],"proposes":[66],"Flow,":[67],"fine-grained":[69],"stateless":[70,118],"approach.":[73],"Flow":[74,99,116,136,162],"creates":[75],"dynamic":[76],"models":[78],"learning":[80],"routing":[82,102],"mechanism":[83],"determines":[85],"whether":[86],"an":[87],"instance":[89],"prefers":[90],"local":[92],"parameters":[93],"or":[94],"its":[95,128],"counterpart.":[97],"Thus,":[98],"introduces":[100],"per-instance":[101],"addition":[104],"leveraging":[106],"per-client":[107,168],"improve":[110],"accuracies":[111],"Further,":[115],"is":[117],"which":[119],"makes":[120,135],"it":[121],"unnecessary":[122],"for":[123,138],"retain":[127],"state":[130],"across":[131],"rounds.":[133],"practical":[137],"large-scale":[139],"settings":[141],"and":[142,152,166],"friendly":[143],"newly":[145],"joined":[146],"clients.":[147],"Evaluations":[148],"on":[149],"Stackoverflow,":[150],"Reddit,":[151],"EMNIST":[153],"datasets":[154],"demonstrate":[155],"superiority":[157],"prediction":[159],"accuracy":[160],"over":[163],"state-of-the-art":[164],"non-personalized":[165],"only":[167],"FL.":[172]},"counts_by_year":[],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2022-12-10T00:00:00"}