{"id":"https://openalex.org/W4402897322","doi":"https://doi.org/10.1109/iwqos61813.2024.10682851","title":"Adaptive Personalized Federated Learning for Non-IID Data with Continual Distribution Shift","display_name":"Adaptive Personalized Federated Learning for Non-IID Data with Continual Distribution Shift","publication_year":2024,"publication_date":"2024-06-19","ids":{"openalex":"https://openalex.org/W4402897322","doi":"https://doi.org/10.1109/iwqos61813.2024.10682851"},"language":"en","primary_location":{"id":"doi:10.1109/iwqos61813.2024.10682851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos61813.2024.10682851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)","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/A5100341521","display_name":"Sisi Chen","orcid":"https://orcid.org/0000-0001-8519-6951"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sisi Chen","raw_affiliation_strings":["Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107952918","display_name":"Weijie Liu","orcid":"https://orcid.org/0009-0007-6403-5332"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijie Liu","raw_affiliation_strings":["Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100763625","display_name":"Xiaoxi Zhang","orcid":"https://orcid.org/0000-0003-0751-2773"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxi Zhang","raw_affiliation_strings":["Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109130170","display_name":"Hong Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Xu","raw_affiliation_strings":["The Chinese University of Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046176565","display_name":"Wanyu Lin","orcid":"https://orcid.org/0000-0002-7328-8039"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wanyu Lin","raw_affiliation_strings":["The Hong Kong Polytechnic University"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014270587","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0002-2978-262X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Chen","raw_affiliation_strings":["Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100341521"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":1.5448,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.85387002,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"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":0.9932000041007996,"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":0.9932000041007996,"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":0.9409000277519226,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6882196664810181},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.49918460845947266},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09792926907539368}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6882196664810181},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.49918460845947266},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09792926907539368},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwqos61813.2024.10682851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos61813.2024.10682851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1647616909","https://openalex.org/W2807006176","https://openalex.org/W2904190483","https://openalex.org/W2954124071","https://openalex.org/W2963163009","https://openalex.org/W2976335444","https://openalex.org/W2990789643","https://openalex.org/W3005776401","https://openalex.org/W3005876528","https://openalex.org/W3006360344","https://openalex.org/W3080934299","https://openalex.org/W3099314130","https://openalex.org/W3194794012","https://openalex.org/W4200580682","https://openalex.org/W4200631596","https://openalex.org/W4229005866","https://openalex.org/W4248415074","https://openalex.org/W4285876308","https://openalex.org/W4287331318","https://openalex.org/W4289147229","https://openalex.org/W4300427714","https://openalex.org/W4318619660","https://openalex.org/W4322718191","https://openalex.org/W4367859878","https://openalex.org/W6728757088","https://openalex.org/W6738383168","https://openalex.org/W6752029299","https://openalex.org/W6756756286","https://openalex.org/W6757172675","https://openalex.org/W6759238902","https://openalex.org/W6762028012","https://openalex.org/W6768570320","https://openalex.org/W6770590064","https://openalex.org/W6773684513","https://openalex.org/W6773813173","https://openalex.org/W6779269186","https://openalex.org/W6784336702","https://openalex.org/W6790358555","https://openalex.org/W6791102956","https://openalex.org/W6800358597","https://openalex.org/W6804522014","https://openalex.org/W6811340617","https://openalex.org/W6850625674","https://openalex.org/W6852464802","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"has":[3],"surged":[4],"in":[5,87],"popularity,":[6],"allowing":[7],"machine":[8],"learning":[9],"models":[10],"to":[11,33,92,123,163,196,218],"be":[12],"collaboratively":[13],"trained":[14],"using":[15,160],"decentralized":[16],"client":[17],"data,":[18,135],"all":[19],"while":[20],"upholding":[21],"privacy":[22],"and":[23,159,172,186,209],"security":[24],"standards.":[25],"However,":[26],"leveraging":[27],"locally-stored":[28],"data":[29,34,55,73,78,101,152],"introduces":[30],"challenges":[31,69],"related":[32],"heterogeneity.":[35],"While":[36],"many":[37],"past":[38],"studies":[39],"have":[40],"addressed":[41],"this":[42,62],"non-IID":[43,77],"problem,":[44],"they":[45],"often":[46],"overlook":[47],"the":[48,68,95,118,125,131,138,156,179,184,198],"dynamic":[49],"nature":[50,97],"of":[51,98,110,189,200],"each":[52,99,161],"individual":[53],"client\u2019s":[54],"or":[56],"disrupt":[57],"its":[58],"continuous":[59],"shift.":[60],"In":[61],"paper,":[63],"our":[64,201,214],"emphasis":[65],"is":[66],"on":[67,130],"posed":[70],"by":[71,103,148,182],"temporal":[72,187],"distribution":[74,126,210],"shift":[75],"alongside":[76],"across":[79,206],"clients,":[80],"a":[81,107,144,165],"more":[82],"prevalent":[83],"yet":[84],"complex":[85],"situation":[86],"real-world":[88],"FL.":[89],"We":[90,115,192],"propose":[91],"analytically":[93],"capture":[94],"evolving":[96],"local":[100],"distribution,":[102],"modeling":[104],"them":[105],"as":[106],"time-varying":[108],"composite":[109],"multiple":[111],"latent":[112],"Gaussian":[113],"distributions.":[114],"then":[116],"employ":[117],"expectation":[119],"maximization":[120],"(EM)":[121],"algorithm":[122,158],"deduce":[124],"model":[127],"parameters":[128],"based":[129],"prevailing":[132],"observed":[133],"training":[134,181,202],"ensuring":[136],"that":[137],"learned":[139],"mixture":[140],"proportion":[141],"weights":[142],"mirror":[143],"consistent":[145],"trajectory.":[146],"Additionally,":[147],"embedding":[149],"an":[150,170],"adaptive":[151],"partitioning":[153],"method":[154],"into":[155],"EM":[157],"partition":[162],"train":[164],"distinct":[166],"sub-model,":[167],"we":[168],"realize":[169],"intuitive":[171],"novel":[173],"personalized":[174],"FL":[175,180],"paradigm.":[176],"This":[177],"refines":[178],"exploiting":[183],"heterogeneity":[185],"shifts":[188],"clients\u2019":[190],"datasets.":[191],"derive":[193],"analytical":[194],"results":[195],"guarantee":[197],"convergence":[199],"method.":[203],"Comprehensive":[204],"tests":[205],"diverse":[207],"datasets":[208],"configurations":[211],"also":[212],"underscore":[213],"enhanced":[215],"efficacy":[216],"compared":[217],"several":[219],"state-of-the-art.":[220]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}