{"id":"https://openalex.org/W3210160361","doi":"https://doi.org/10.1109/iicaiet51634.2021.9573567","title":"Federated Learning: Optimizing Objective Function","display_name":"Federated Learning: Optimizing Objective Function","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W3210160361","doi":"https://doi.org/10.1109/iicaiet51634.2021.9573567","mag":"3210160361"},"language":"en","primary_location":{"id":"doi:10.1109/iicaiet51634.2021.9573567","doi":"https://doi.org/10.1109/iicaiet51634.2021.9573567","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iicaiet51634.2021.9573567","pdf_url":null,"source":{"id":"https://openalex.org/S4363608273","display_name":"IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085934133","display_name":"Aishwarya Asesh","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Aishwarya Asesh","raw_affiliation_strings":["Adobe, Lehi, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Adobe, Lehi, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5085934133"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1865,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.36302796,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":95},"biblio":{"volume":"97","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9994999766349792,"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":0.9994999766349792,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.00039999998989515007,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/federated-learning","display_name":"federated learning","score":1.0},{"id":"https://openalex.org/keywords/sparse-gradients","display_name":"sparse gradients","score":0.6610000133514404},{"id":"https://openalex.org/keywords/data-heterogeneity","display_name":"data heterogeneity","score":0.7139999866485596},{"id":"https://openalex.org/keywords/convergence-rate","display_name":"convergence rate","score":0.7829999923706055},{"id":"https://openalex.org/keywords/data-randomization","display_name":"data randomization","score":0.7799999713897705},{"id":"https://openalex.org/keywords/client-sampling","display_name":"client sampling","score":0.6169999837875366},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"distributed learning","score":0.4169999957084656},{"id":"https://openalex.org/keywords/imputed-data","display_name":"imputed data","score":0.5440000295639038}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.856859564781189},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.667375922203064},{"id":"https://openalex.org/C2780821482","wikidata":"https://www.wikidata.org/wiki/Q25381721","display_name":"Crowdsensing","level":2,"score":0.5289300084114075},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.4969628155231476},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4841116666793823},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4649679958820343},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.42581111192703247},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4212481677532196},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40800586342811584},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2598487436771393},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.142899751663208},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iicaiet51634.2021.9573567","doi":"https://doi.org/10.1109/iicaiet51634.2021.9573567","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iicaiet51634.2021.9573567","pdf_url":null,"source":{"id":"https://openalex.org/S4363608273","display_name":"IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W150223756","https://openalex.org/W2053637704","https://openalex.org/W2083026317","https://openalex.org/W2092422002","https://openalex.org/W2128906841","https://openalex.org/W2541884796","https://openalex.org/W2618494520","https://openalex.org/W2767079719","https://openalex.org/W2781091734","https://openalex.org/W2807006176","https://openalex.org/W2894972989","https://openalex.org/W2912322140","https://openalex.org/W2946633206","https://openalex.org/W2955213239","https://openalex.org/W2962726660","https://openalex.org/W2963300197","https://openalex.org/W2964123455","https://openalex.org/W2995022099","https://openalex.org/W2995653155","https://openalex.org/W3010968654","https://openalex.org/W3035038335","https://openalex.org/W3091870957","https://openalex.org/W3141585064","https://openalex.org/W4248358572"],"related_works":["https://openalex.org/W7162318595","https://openalex.org/W2949377959","https://openalex.org/W4327650128","https://openalex.org/W7084124479","https://openalex.org/W3129329365","https://openalex.org/W4361230999","https://openalex.org/W4315629593","https://openalex.org/W4385801067","https://openalex.org/W3129682320","https://openalex.org/W4389458298"],"abstract_inverted_index":{"A":[0],"universal":[1],"server":[2],"coordinates":[3],"the":[4,78,97,112,115,152,166,183],"training":[5],"of":[6,15,72,99,114,133,155,168],"a":[7,11,27,50,173],"single":[8],"model":[9],"on":[10,84,111],"largely":[12],"distributed":[13,169],"network":[14],"computers":[16],"in":[17,31],"federated":[18,36,56,82,86,116],"learning.":[19],"This":[20,65],"setting":[21],"can":[22],"easily":[23],"be":[24],"expanded":[25],"to":[26,33,62,76,163,175],"multi-task":[28,57],"learning":[29,45,58,83,117],"system":[30],"order":[32],"manage":[34],"real-world":[35,53,85],"datasets":[37,87],"with":[38],"high":[39],"statistical":[40],"heterogeneity":[41,145],"across":[42],"devices.":[43],"Federated":[44],"is":[46,141,151],"very":[47],"useful":[48],"as":[49,88,90],"framework":[51],"for":[52,81],"data":[54,92,101,103,144],"and":[55,69,105,125,138,140,178],"has":[59],"been":[60],"applied":[61],"convex":[63],"models.":[64],"research":[66,150],"work":[67],"discusses":[68],"evaluates":[70],"possibility":[71],"sparser":[73],"gradient":[74,126],"changes":[75],"outperform":[77],"existing":[79],"state-of-the-art":[80],"well":[89],"imputed":[91],"values.":[93],"The":[94,119,149],"experiments":[95],"investigate":[96],"effect":[98],"rolling":[100],"or":[102,146],"randomization":[104],"adaptive":[106],"global":[107],"frequency":[108],"update":[109],"scheduling":[110],"convergence":[113,123],"algorithm.":[118],"results":[120],"show":[121],"that":[122,160],"speed":[124],"curve":[127],"are":[128],"considerably":[129],"affected":[130],"by":[131,143,171],"number":[132],"contact":[134],"rounds":[135],"between":[136],"worker":[137],"aggregator":[139],"unaffected":[142],"client":[147],"sampling.":[148],"core":[153],"part":[154],"an":[156],"extended":[157],"experimental":[158],"setup":[159],"will":[161],"follow":[162],"better":[164],"understand":[165],"behavior":[167],"learning,":[170],"developing":[172],"simulation":[174],"track":[176],"weights":[177],"loss":[179],"function":[180],"gradients":[181],"during":[182],"training.":[184]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-11-08T00:00:00"}