{"id":"https://openalex.org/W7116275824","doi":"https://doi.org/10.1145/3754598.3754657","title":"FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios","display_name":"FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W7116275824","doi":"https://doi.org/10.1145/3754598.3754657"},"language":null,"primary_location":{"id":"doi:10.1145/3754598.3754657","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3754598.3754657","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 54th International Conference on Parallel Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3754598.3754657","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031826523","display_name":"T. L. Li","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianle Li","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0003-5535-0577","affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yongzhi Huang","orcid":"https://orcid.org/0000-0003-3728-3502"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yongzhi Huang","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-3728-3502","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048918787","display_name":"Linshan Jiang","orcid":"https://orcid.org/0000-0001-8501-9488"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Linshan Jiang","raw_affiliation_strings":["National University of Singapore, Singapore city, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-8501-9488","affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore city, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120902311","display_name":"Qipeng Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qipeng Xie","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-5500-0249","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chang Liu","orcid":"https://orcid.org/0000-0003-3767-6533"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Chang Liu","raw_affiliation_strings":["Nanyang Technological University, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0003-3767-6533","affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Zhejiang, China","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120921747","display_name":"Wenfeng Du","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenfeng Du","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0002-9883-0311","affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lu Wang","orcid":"https://orcid.org/0000-0001-6345-3873"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lu Wang","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-6345-3873","affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":null,"display_name":"Kaishun Wu","orcid":"https://orcid.org/0000-0003-2216-0737"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaishun Wu","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2216-0737","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5031826523"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.79399412,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"73","last_page":"82"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.8682000041007996,"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":0.8682000041007996,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.027499999850988388,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.010599999688565731,"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":0.6776999831199646},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5415999889373779},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4490000009536743},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.35839998722076416},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.35030001401901245},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.34279999136924744},{"id":"https://openalex.org/keywords/momentum","display_name":"Momentum (technical analysis)","score":0.33709999918937683}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7627999782562256},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6776999831199646},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5415999889373779},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4490000009536743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43320000171661377},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.38920000195503235},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3711000084877014},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3479999899864197},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C60718061","wikidata":"https://www.wikidata.org/wiki/Q1414747","display_name":"Momentum (technical analysis)","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.33340001106262207},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3197000026702881},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C47487241","wikidata":"https://www.wikidata.org/wiki/Q5227230","display_name":"Data access","level":2,"score":0.2678000032901764}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3754598.3754657","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3754598.3754657","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 54th International Conference on Parallel Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3754598.3754657","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3754598.3754657","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 54th International Conference on Parallel Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2118978333","https://openalex.org/W2194775991","https://openalex.org/W2954124071","https://openalex.org/W2963351448","https://openalex.org/W3042621011","https://openalex.org/W3080934299","https://openalex.org/W3128382687","https://openalex.org/W3152412642","https://openalex.org/W3177200443","https://openalex.org/W3196371845","https://openalex.org/W3204009762","https://openalex.org/W3205278400","https://openalex.org/W3209598599","https://openalex.org/W4285603347","https://openalex.org/W4285815152","https://openalex.org/W4360995044","https://openalex.org/W4386076042","https://openalex.org/W4386597570","https://openalex.org/W4393147298","https://openalex.org/W4398202254"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"enables":[3],"decentralized":[4],"model":[5],"training":[6],"while":[7],"preserving":[8],"data":[9,90,121],"privacy.":[10],"Despite":[11],"its":[12],"benefits,":[13],"FL":[14,31,37,49],"faces":[15],"challenges":[16],"with":[17,26,40],"non-identically":[18],"distributed":[19],"(non-IID)":[20],"data,":[21],"especially":[22],"in":[23,44,116],"long-tailed":[24,97],"scenarios":[25],"imbalanced":[27],"class":[28],"samples.":[29],"Momentum-based":[30],"methods,":[32,110],"often":[33],"used":[34],"to":[35,51,91],"accelerate":[36],"convergence,":[38],"struggle":[39],"these":[41,75],"distributions,":[42],"resulting":[43],"biased":[45],"models":[46],"and":[47,88,107,114,120],"making":[48],"hard":[50],"converge.":[52],"To":[53],"understand":[54],"this":[55,62],"challenge,":[56],"we":[57,77],"conduct":[58],"extensive":[59],"investigations":[60],"into":[61],"phenomenon,":[63],"accompanied":[64],"by":[65,96],"a":[66,80],"layer-wise":[67],"analysis":[68],"of":[69],"neural":[70],"network":[71],"behavior.":[72],"Based":[73],"on":[74],"insights,":[76],"propose":[78],"FedWCM,":[79],"method":[81],"that":[82,102],"dynamically":[83],"adjusts":[84],"momentum":[85],"using":[86],"global":[87],"per-round":[89],"correct":[92],"directional":[93],"biases":[94],"introduced":[95],"distributions.":[98],"Extensive":[99],"experiments":[100],"show":[101],"FedWCM":[103],"resolves":[104],"non-convergence":[105],"issues":[106],"outperforms":[108],"existing":[109],"enhancing":[111],"FL\u2019s":[112],"efficiency":[113],"effectiveness":[115],"handling":[117],"client":[118],"heterogeneity":[119],"imbalance.":[122]},"counts_by_year":[],"updated_date":"2025-12-21T02:06:08.432651","created_date":"2025-12-21T00:00:00"}