{"id":"https://openalex.org/W7125954700","doi":"https://doi.org/10.1109/smc58881.2025.11342818","title":"FedPAC: A Federated Semi-Supervised Learning Approach for Non-IID Data with Feature Shift","display_name":"FedPAC: A Federated Semi-Supervised Learning Approach for Non-IID Data with Feature Shift","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125954700","doi":"https://doi.org/10.1109/smc58881.2025.11342818"},"language":"en","primary_location":{"id":"doi:10.1109/smc58881.2025.11342818","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342818","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure-oai.bham.ac.uk/ws/files/269520874/Xudong_SMC2025.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124059733","display_name":"Xudong Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xudong Guo","raw_affiliation_strings":["Yunnan University,National Pilot School of Software,Kunming,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University,National Pilot School of Software,Kunming,China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122066496","display_name":"Shuo Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shuo Wang","raw_affiliation_strings":["the University of Birmingham,School of Computer Science,Birmingham,UK,B15 2TT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"the University of Birmingham,School of Computer Science,Birmingham,UK,B15 2TT","institution_ids":["https://openalex.org/I79619799"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.65581169,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2809","last_page":"2814"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9308000206947327,"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":0.9308000206947327,"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":0.00839999970048666,"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/T10057","display_name":"Face and Expression Recognition","score":0.00570000009611249,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/feature","display_name":"Feature (linguistics)","score":0.7635999917984009},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.699400007724762},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5809999704360962},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5519999861717224},{"id":"https://openalex.org/keywords/feature-model","display_name":"Feature model","score":0.48089998960494995},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4408999979496002},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43709999322891235},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.4074000120162964},{"id":"https://openalex.org/keywords/data-sharing","display_name":"Data sharing","score":0.36809998750686646}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786300003528595},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7635999917984009},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.699400007724762},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5809999704360962},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5539000034332275},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5519999861717224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48249998688697815},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.48089998960494995},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4408999979496002},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43709999322891235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41179999709129333},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.4074000120162964},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.36809998750686646},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.26579999923706055},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/smc58881.2025.11342818","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342818","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:openaire/67c4bc21-9171-4646-867d-caeb52c4a0c1","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/67c4bc21-9171-4646-867d-caeb52c4a0c1","pdf_url":"https://pure-oai.bham.ac.uk/ws/files/269520874/Xudong_SMC2025.pdf","source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Guo, X & Wang, S 2026, FedPAC : A Federated Semi-Supervised Learning Approach for Non-IID Data with Feature Shift. in 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE International Conference on Systems, Man and Cybernetics, IEEE, pp. 2809-2814, 2025 IEEE International Conference on Systems, Man and Cybernetics, Vienna, Austria, 5/10/25. https://doi.org/10.1109/SMC58881.2025.11342818","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:openaire/67c4bc21-9171-4646-867d-caeb52c4a0c1","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/67c4bc21-9171-4646-867d-caeb52c4a0c1","pdf_url":"https://pure-oai.bham.ac.uk/ws/files/269520874/Xudong_SMC2025.pdf","source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Guo, X & Wang, S 2026, FedPAC : A Federated Semi-Supervised Learning Approach for Non-IID Data with Feature Shift. in 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE International Conference on Systems, Man and Cybernetics, IEEE, pp. 2809-2814, 2025 IEEE International Conference on Systems, Man and Cybernetics, Vienna, Austria, 5/10/25. https://doi.org/10.1109/SMC58881.2025.11342818","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6606379827","display_name":"Adaptive Multi-Source Transfer Learning Approaches for Environmental Challenges","funder_award_id":"EP/Y002539/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G6652630408","display_name":null,"funder_award_id":"62206239","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7125954700.pdf","grobid_xml":"https://content.openalex.org/works/W7125954700.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W2131953535","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2903890850","https://openalex.org/W2912213068","https://openalex.org/W2918552801","https://openalex.org/W2984353870","https://openalex.org/W2995022099","https://openalex.org/W3015636663","https://openalex.org/W3100779497","https://openalex.org/W3127057363","https://openalex.org/W3171581326","https://openalex.org/W3204597713","https://openalex.org/W4225978071","https://openalex.org/W4287332481","https://openalex.org/W4312436424","https://openalex.org/W4312462223","https://openalex.org/W4386075641","https://openalex.org/W4393154273","https://openalex.org/W4402436199"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"Semi-Supervised":[1],"Learning":[2],"(FSSL)":[3],"enables":[4],"collaborative":[5],"model":[6],"training":[7],"across":[8],"distributed":[9],"clients":[10,29],"with":[11],"limited":[12],"labeled":[13],"data":[14,17],"while":[15],"preserving":[16],"privacy.":[18],"However,":[19],"a":[20,46,104],"critical":[21],"challenge":[22],"in":[23,98],"FSSL":[24,48,96],"is":[25],"feature":[26,32,62,67,80,99],"shift,":[27],"where":[28],"exhibit":[30],"diverse":[31],"distributions":[33,68],"despite":[34],"sharing":[35],"the":[36],"same":[37],"task.":[38],"To":[39],"address":[40],"this":[41],"issue,":[42],"we":[43],"propose":[44],"FedPAC,":[45],"novel":[47],"framework":[49,60],"that":[50,92],"integrates":[51],"Contrastive":[52],"Mean-Teacher":[53],"Regularization":[54],"and":[55,71,74,114],"Perturbation-Aware":[56],"Gradient":[57],"Descent.":[58],"Our":[59],"enhances":[61],"representation":[63],"learning":[64],"by":[65,79],"aligning":[66],"between":[69],"teacher":[70],"student":[72],"models":[73],"mitigates":[75],"optimization":[76],"challenges":[77],"caused":[78],"heterogeneity":[81],"through":[82],"controlled":[83],"gradient":[84],"perturbations.":[85],"Extensive":[86],"experiments":[87],"on":[88],"benchmark":[89],"datasets":[90],"demonstrate":[91],"FedPAC":[93],"outperforms":[94],"existing":[95],"methods":[97],"shift":[100],"scenarios,":[101],"making":[102],"it":[103],"practical":[105],"solution":[106],"for":[107],"real-world":[108],"applications":[109],"such":[110],"as":[111],"medical":[112],"imaging":[113],"industrial":[115],"fault":[116],"diagnosis.":[117]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2026-01-29T00:00:00"}
