{"id":"https://openalex.org/W4417073390","doi":"https://doi.org/10.1145/3743093.3771067","title":"Task-Aware Federated Multi-Task Learning","display_name":"Task-Aware Federated Multi-Task Learning","publication_year":2025,"publication_date":"2025-12-06","ids":{"openalex":"https://openalex.org/W4417073390","doi":"https://doi.org/10.1145/3743093.3771067"},"language":null,"primary_location":{"id":"doi:10.1145/3743093.3771067","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3743093.3771067","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3743093.3771067","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3743093.3771067","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lei Li","orcid":"https://orcid.org/0009-0003-4317-9071"},"institutions":[{"id":"https://openalex.org/I102607778","display_name":"Cleveland State University","ror":"https://ror.org/002tx1f22","country_code":"US","type":"education","lineage":["https://openalex.org/I102607778"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lei Li","raw_affiliation_strings":["Cleveland State University, Cleveland, Ohio, USA"],"raw_orcid":"https://orcid.org/0009-0003-4317-9071","affiliations":[{"raw_affiliation_string":"Cleveland State University, Cleveland, Ohio, USA","institution_ids":["https://openalex.org/I102607778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087813864","display_name":"Haochen Yang","orcid":"https://orcid.org/0000-0001-9145-8575"},"institutions":[{"id":"https://openalex.org/I102607778","display_name":"Cleveland State University","ror":"https://ror.org/002tx1f22","country_code":"US","type":"education","lineage":["https://openalex.org/I102607778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haochen Yang","raw_affiliation_strings":["Cleveland State University, Cleveland, Ohio, USA"],"raw_orcid":"https://orcid.org/0000-0001-9145-8575","affiliations":[{"raw_affiliation_string":"Cleveland State University, Cleveland, Ohio, USA","institution_ids":["https://openalex.org/I102607778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114080411","display_name":"Jiacheng Guo","orcid":"https://orcid.org/0009-0006-8508-9949"},"institutions":[{"id":"https://openalex.org/I102607778","display_name":"Cleveland State University","ror":"https://ror.org/002tx1f22","country_code":"US","type":"education","lineage":["https://openalex.org/I102607778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiacheng Guo","raw_affiliation_strings":["Cleveland State University, Cleveland, Ohio, USA"],"raw_orcid":"https://orcid.org/0009-0006-8508-9949","affiliations":[{"raw_affiliation_string":"Cleveland State University, Cleveland, Ohio, USA","institution_ids":["https://openalex.org/I102607778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025512337","display_name":"Hongkai Yu","orcid":"https://orcid.org/0000-0001-5383-8913"},"institutions":[{"id":"https://openalex.org/I102607778","display_name":"Cleveland State University","ror":"https://ror.org/002tx1f22","country_code":"US","type":"education","lineage":["https://openalex.org/I102607778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongkai Yu","raw_affiliation_strings":["Cleveland State University, Cleveland, Ohio, USA"],"raw_orcid":"https://orcid.org/0000-0001-5383-8913","affiliations":[{"raw_affiliation_string":"Cleveland State University, Cleveland, Ohio, USA","institution_ids":["https://openalex.org/I102607778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084528785","display_name":"Minghai Qin","orcid":"https://orcid.org/0000-0001-5172-5309"},"institutions":[{"id":"https://openalex.org/I102607778","display_name":"Cleveland State University","ror":"https://ror.org/002tx1f22","country_code":"US","type":"education","lineage":["https://openalex.org/I102607778"]},{"id":"https://openalex.org/I4210121352","display_name":"Western Digital (United States)","ror":"https://ror.org/02hqwnx33","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121352"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minghai Qin","raw_affiliation_strings":["Western Digital Corporation, San Jose, California, USA and Cleveland State University, Cleveland, Ohio, USA"],"raw_orcid":"https://orcid.org/0000-0001-5172-5309","affiliations":[{"raw_affiliation_string":"Western Digital Corporation, San Jose, California, USA and Cleveland State University, Cleveland, Ohio, USA","institution_ids":["https://openalex.org/I102607778","https://openalex.org/I4210121352"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101507891","display_name":"Tianyun Zhang","orcid":"https://orcid.org/0000-0002-2475-6414"},"institutions":[{"id":"https://openalex.org/I102607778","display_name":"Cleveland State University","ror":"https://ror.org/002tx1f22","country_code":"US","type":"education","lineage":["https://openalex.org/I102607778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyun Zhang","raw_affiliation_strings":["Cleveland State University, Cleveland, Ohio, USA"],"raw_orcid":"https://orcid.org/0000-0002-2475-6414","affiliations":[{"raw_affiliation_string":"Cleveland State University, Cleveland, Ohio, USA","institution_ids":["https://openalex.org/I102607778"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I102607778"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19862049,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.7491000294685364,"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":0.7491000294685364,"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":0.04809999838471413,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.01769999973475933,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/scalability","display_name":"Scalability","score":0.8122000098228455},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6312999725341797},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6110000014305115},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6092000007629395},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5629000067710876},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.5368000268936157},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5019999742507935},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4560000002384186},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.4049000144004822}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8432000279426575},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.8122000098228455},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.635200023651123},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6312999725341797},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6110000014305115},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6092000007629395},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5629000067710876},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.5368000268936157},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5019999742507935},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4560000002384186},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C48798503","wikidata":"https://www.wikidata.org/wiki/Q877546","display_name":"Reputation","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3684999942779541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34689998626708984},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.32659998536109924},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.320499986410141},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.2671000063419342},{"id":"https://openalex.org/C138020889","wikidata":"https://www.wikidata.org/wiki/Q2349659","display_name":"Collaborative learning","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.263700008392334},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3743093.3771067","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3743093.3771067","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3743093.3771067","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3743093.3771067","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3743093.3771067","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3743093.3771067","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1038859201","display_name":null,"funder_award_id":"CNS-2245765","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":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417073390.pdf","grobid_xml":"https://content.openalex.org/works/W4417073390.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W1873763122","https://openalex.org/W2053637704","https://openalex.org/W2531409750","https://openalex.org/W2913340405","https://openalex.org/W2963049063","https://openalex.org/W2964185501","https://openalex.org/W2995022099","https://openalex.org/W3018102029","https://openalex.org/W3080934299","https://openalex.org/W3141797743","https://openalex.org/W3188011552","https://openalex.org/W4225796852","https://openalex.org/W4244393449","https://openalex.org/W4385801067","https://openalex.org/W4390873358","https://openalex.org/W4402727840","https://openalex.org/W4405034471"],"related_works":[],"abstract_inverted_index":{"Federated":[0,103],"Multi-Task":[1,104],"Learning":[2,105],"(FMTL)":[3],"enables":[4,154],"collaborative":[5],"training":[6],"of":[7],"multiple":[8],"tasks":[9,23,68,132,159],"across":[10,157],"decentralized":[11],"clients,":[12],"but":[13,72],"faces":[14],"two":[15,117],"key":[16],"challenges":[17],"in":[18,192],"practice:":[19],"negative":[20,94],"transfer":[21],"among":[22],"and":[24,42,76,89,113,136,188,195,201,208],"scalability":[25,200],"under":[26,206],"resource":[27],"constraints.":[28],"Task":[29],"differences":[30,88],"can":[31],"cause":[32],"gradient":[33],"conflicts":[34],"that":[35,110,125,141,169],"degrade":[36],"overall":[37,146],"performance,":[38],"while":[39,181],"limited":[40],"computation":[41,77],"storage":[43],"on":[44,164],"edge":[45],"devices":[46],"make":[47],"it":[48],"difficult":[49],"to":[50,69,91,130,177,186],"maintain":[51],"accuracy":[52,112,176],"with":[53,144],"low":[54],"overhead.":[55],"Existing":[56],"methods":[57],"address":[58],"these":[59,98],"issues":[60],"either":[61],"by":[62,80,184,190],"adopting":[63],"multi-backbone":[64],"architectures,":[65],"which":[66,85],"split":[67],"reduce":[70],"interference":[71],"incur":[73],"substantial":[74],"parameter":[75],"costs,":[78],"or":[79,148,174],"performing":[81],"naive":[82],"global":[83],"averaging,":[84],"ignores":[86],"inter-task":[87],"fails":[90],"effectively":[92],"mitigate":[93],"transfer.":[95],"To":[96],"overcome":[97],"limitations,":[99],"we":[100],"propose":[101],"Task-Aware":[102],"(TA-FMTL),":[106],"a":[107,120,137],"single-backbone":[108],"framework":[109],"balances":[111],"efficiency.":[114],"TA-FMTL":[115,170],"integrates":[116],"lightweight":[118],"components:":[119],"min\u2013max":[121],"task-difficulty":[122],"weighting":[123],"strategy":[124],"dynamically":[126],"allocates":[127],"more":[128],"updates":[129],"harder":[131],"for":[133,203],"balanced":[134],"optimization,":[135],"variance-aware":[138],"reputation":[139],"aggregation":[140],"down-weights":[142],"clients":[143],"high":[145],"loss":[147],"unstable":[149],"cross-task":[150],"performance.":[151],"This":[152],"design":[153],"robust":[155],"coordination":[156],"heterogeneous":[158,207],"without":[160],"task":[161],"splitting.":[162],"Experiments":[163],"the":[165],"Taskonomy":[166],"benchmark":[167],"show":[168],"consistently":[171],"achieves":[172],"better":[173],"comparable":[175],"state-of-the-art":[178],"MAS":[179],"variants":[180],"reducing":[182],"parameters":[183],"up":[185],"77.2%":[187],"FLOPs":[189],"37.3%":[191],"challenging":[193],"5-task":[194],"9-task":[196],"settings,":[197],"demonstrating":[198],"its":[199],"practicality":[202],"real-world":[204],"FMTL":[205],"resource-limited":[209],"conditions.":[210]},"counts_by_year":[],"updated_date":"2026-03-10T14:07:55.174380","created_date":"2025-12-06T00:00:00"}