{"id":"https://openalex.org/W4406458019","doi":"https://doi.org/10.1109/bigdata62323.2024.10825575","title":"Personalized Federated Learning Techniques: Empirical Analysis","display_name":"Personalized Federated Learning Techniques: Empirical Analysis","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458019","doi":"https://doi.org/10.1109/bigdata62323.2024.10825575"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","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/A5063807294","display_name":"Azal Ahmad Khan","orcid":"https://orcid.org/0009-0000-9435-5328"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]},{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]},{"id":"https://openalex.org/I4210101327","display_name":"Twin Cities Orthopedics","ror":"https://ror.org/01en4s460","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210101327"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Azal Ahmad Khan","raw_affiliation_strings":["University of Minnesota-Twin Cities,Department of Computer Science and Engineering","Virginia Tech,Department of Computer Science"],"affiliations":[{"raw_affiliation_string":"University of Minnesota-Twin Cities,Department of Computer Science and Engineering","institution_ids":["https://openalex.org/I4210101327","https://openalex.org/I130238516"]},{"raw_affiliation_string":"Virginia Tech,Department of Computer Science","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050886354","display_name":"Ahmad Khan","orcid":"https://orcid.org/0000-0002-6955-8876"},"institutions":[{"id":"https://openalex.org/I4210101327","display_name":"Twin Cities Orthopedics","ror":"https://ror.org/01en4s460","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210101327"]},{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]},{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmad Faraz Khan","raw_affiliation_strings":["University of Minnesota-Twin Cities,Department of Computer Science and Engineering","Virginia Tech,Department of Computer Science"],"affiliations":[{"raw_affiliation_string":"University of Minnesota-Twin Cities,Department of Computer Science and Engineering","institution_ids":["https://openalex.org/I4210101327","https://openalex.org/I130238516"]},{"raw_affiliation_string":"Virginia Tech,Department of Computer Science","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036819966","display_name":"Haidar Ibrahim Ali","orcid":null},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haidar Ali","raw_affiliation_strings":["Virginia Tech,Department of Computer Science"],"affiliations":[{"raw_affiliation_string":"Virginia Tech,Department of Computer Science","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054645319","display_name":"Ali Anwar","orcid":"https://orcid.org/0000-0003-4487-2436"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]},{"id":"https://openalex.org/I4210101327","display_name":"Twin Cities Orthopedics","ror":"https://ror.org/01en4s460","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210101327"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Anwar","raw_affiliation_strings":["University of Minnesota-Twin Cities,Department of Computer Science and Engineering"],"affiliations":[{"raw_affiliation_string":"University of Minnesota-Twin Cities,Department of Computer Science and Engineering","institution_ids":["https://openalex.org/I4210101327","https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063807294"],"corresponding_institution_ids":["https://openalex.org/I130238516","https://openalex.org/I4210101327","https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":1.0878,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.82756118,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1333","last_page":"1339"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9855999946594238,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9843999743461609,"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":0.793333888053894},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.45353201031684875},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.3690061569213867}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.793333888053894},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.45353201031684875},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3690061569213867}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W2535838896","https://openalex.org/W2900120080","https://openalex.org/W2904760378","https://openalex.org/W2963819344","https://openalex.org/W2976335444","https://openalex.org/W2990789643","https://openalex.org/W3007345209","https://openalex.org/W3012501605","https://openalex.org/W3021654819","https://openalex.org/W3086590218","https://openalex.org/W3089578458","https://openalex.org/W3097714942","https://openalex.org/W3125494587","https://openalex.org/W3129603732","https://openalex.org/W3133814152","https://openalex.org/W3159333625","https://openalex.org/W3168269241","https://openalex.org/W3186643354","https://openalex.org/W3196371845","https://openalex.org/W4214758645","https://openalex.org/W4226271719","https://openalex.org/W4283796083","https://openalex.org/W4285071899","https://openalex.org/W4285601288","https://openalex.org/W4285762978","https://openalex.org/W4287064683","https://openalex.org/W4297687186","https://openalex.org/W4366342102","https://openalex.org/W4382463479","https://openalex.org/W4393061146","https://openalex.org/W4394923525","https://openalex.org/W6728757088","https://openalex.org/W6736057607","https://openalex.org/W6755988804","https://openalex.org/W6759226220","https://openalex.org/W6768570320","https://openalex.org/W6770590064","https://openalex.org/W6774120287","https://openalex.org/W6779174293","https://openalex.org/W6786597537","https://openalex.org/W6789305514","https://openalex.org/W6791102956","https://openalex.org/W6791444617","https://openalex.org/W6795165426","https://openalex.org/W6796504275","https://openalex.org/W6798383822","https://openalex.org/W6839362776","https://openalex.org/W6851629957"],"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":{"Personalized":[0],"Federated":[1],"Learning":[2],"(pFL)":[3],"holds":[4],"immense":[5],"promise":[6],"for":[7,50,55],"tailoring":[8],"machine":[9],"learning":[10,119],"models":[11],"to":[12,97],"individual":[13],"users":[14],"while":[15,117],"preserving":[16],"data":[17,70,111],"privacy.":[18],"However,":[19],"achieving":[20],"optimal":[21],"performance":[22],"in":[23,45,75,100,109,142,153],"pFL":[24,64,85],"often":[25],"requires":[26],"a":[27],"careful":[28],"balancing":[29],"act":[30],"between":[31],"memory":[32],"overhead":[33],"costs":[34],"and":[35,69,102,113,130],"model":[36],"accuracy.":[37],"This":[38],"paper":[39],"delves":[40],"into":[41,83],"the":[42,52,93,125,136],"trade-offs":[43],"inherent":[44],"pFL,":[46,144],"offering":[47],"valuable":[48],"insights":[49,82],"selecting":[51],"right":[53],"algorithms":[54],"diverse":[56],"real-world":[57,154],"scenarios.":[58],"We":[59],"empirically":[60],"evaluate":[61],"ten":[62],"prominent":[63],"techniques":[65],"across":[66],"various":[67],"datasets":[68],"splits,":[71],"uncovering":[72],"significant":[73],"differences":[74],"their":[76,98],"performance.":[77],"Our":[78,133],"study":[79,134],"reveals":[80],"interesting":[81],"how":[84,146],"methods":[86,106,120],"that":[87],"utilize":[88],"personalized":[89],"(local)":[90],"aggregation":[91],"exhibit":[92],"fastest":[94],"convergence":[95],"due":[96],"efficiency":[99,141],"communication":[101,140],"computation.":[103],"Conversely,":[104],"fine-tuning":[105],"face":[107],"limitations":[108],"handling":[110],"heterogeneity":[112],"potential":[114],"adversarial":[115],"attacks":[116],"multi-objective":[118],"achieve":[121],"higher":[122],"accuracy":[123],"at":[124],"cost":[126],"of":[127,139],"additional":[128],"training":[129],"resource":[131,151],"consumption.":[132],"emphasizes":[135],"critical":[137],"role":[138],"scaling":[143],"demonstrating":[145],"it":[147],"can":[148],"significantly":[149],"affect":[150],"usage":[152],"deployments.":[155]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
