{"id":"https://openalex.org/W4368353176","doi":"https://doi.org/10.1145/3578356.3592592","title":"Can Fair Federated Learning Reduce the need for Personalisation?","display_name":"Can Fair Federated Learning Reduce the need for Personalisation?","publication_year":2023,"publication_date":"2023-05-04","ids":{"openalex":"https://openalex.org/W4368353176","doi":"https://doi.org/10.1145/3578356.3592592"},"language":"en","primary_location":{"id":"doi:10.1145/3578356.3592592","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3578356.3592592","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3578356.3592592","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd Workshop on Machine Learning and Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3578356.3592592","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086333889","display_name":"Alex Iacob","orcid":"https://orcid.org/0009-0006-8293-5628"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alex Iacob","raw_affiliation_strings":["University of Cambridge, Cambridge, United Kingdom"],"raw_orcid":"https://orcid.org/0009-0006-8293-5628","affiliations":[{"raw_affiliation_string":"University of Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077645636","display_name":"Pedro Gusmao","orcid":"https://orcid.org/0000-0002-7072-9898"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Pedro Porto Buarque Gusm\u00e3o","raw_affiliation_strings":["University of Cambridge, Cambridge, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-7072-9898","affiliations":[{"raw_affiliation_string":"University of Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045638679","display_name":"Nicholas D. Lane","orcid":"https://orcid.org/0000-0002-2728-8273"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nicholas Lane","raw_affiliation_strings":["University of Cambridge, Cambridge, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-2728-8273","affiliations":[{"raw_affiliation_string":"University of Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I241749"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"6368","issue":null,"first_page":"131","last_page":"139"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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/T11045","display_name":"Privacy, Security, and Data Protection","score":0.987500011920929,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9761000275611877,"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/personalization","display_name":"Personalization","score":0.8333752155303955},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8306920528411865},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6874656677246094},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6499371528625488},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5672454237937927},{"id":"https://openalex.org/keywords/incentive","display_name":"Incentive","score":0.4897429943084717},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44921261072158813},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42192697525024414},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.21438929438591003}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.8333752155303955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8306920528411865},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6874656677246094},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6499371528625488},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5672454237937927},{"id":"https://openalex.org/C29122968","wikidata":"https://www.wikidata.org/wiki/Q1414816","display_name":"Incentive","level":2,"score":0.4897429943084717},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44921261072158813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42192697525024414},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.21438929438591003},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3578356.3592592","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3578356.3592592","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3578356.3592592","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd Workshop on Machine Learning and Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2305.02728","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.02728","pdf_url":"https://arxiv.org/pdf/2305.02728","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3578356.3592592","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3578356.3592592","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3578356.3592592","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd Workshop on Machine Learning and Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4368353176.pdf","grobid_xml":"https://content.openalex.org/works/W4368353176.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W1965555277","https://openalex.org/W2605800822","https://openalex.org/W2995022099","https://openalex.org/W3016632787","https://openalex.org/W3021654819","https://openalex.org/W4253232812"],"related_works":["https://openalex.org/W4298221930","https://openalex.org/W2109940557","https://openalex.org/W2466832359","https://openalex.org/W4391210591","https://openalex.org/W1582019636","https://openalex.org/W2367468089","https://openalex.org/W2358591856","https://openalex.org/W1499005795","https://openalex.org/W2777914285","https://openalex.org/W4303448918"],"abstract_inverted_index":{"Federated":[0,149],"Learning":[1,150],"(FL)":[2],"enables":[3],"training":[4],"ML":[5],"models":[6],"on":[7,18,39],"edge":[8],"clients":[9,26,40,140,173,184],"without":[10],"sharing":[11],"data.":[12],"However,":[13],"the":[14,23,48,68,86,89,101,136,169,175,180,186],"federated":[15,69,91],"model's":[16],"performance":[17,128],"local":[19,102],"data":[20],"varies,":[21],"disincentivising":[22],"participation":[24,53],"of":[25,88,100,138,171,182,190,203],"who":[27],"benefit":[28,125,206],"little":[29],"from":[30,207],"FL.":[31],"Fair":[32,109],"FL":[33,57,110,208],"reduces":[34],"accuracy":[35,74,87],"disparity":[36],"by":[37,81],"focusing":[38],"with":[41],"higher":[42],"losses":[43,159],"while":[44,178],"personalisation":[45,84,158],"locally":[46,80],"fine-tunes":[47],"model.":[49,104],"Personalisation":[50],"provides":[51,71,123],"a":[52,72,76,82,130,153,165,200,211],"incentive":[54],"when":[55],"an":[56,142],"model":[58,70,77],"underperforms":[59],"relative":[60,127],"to":[61,93,96,126,205],"one":[62],"trained":[63,78],"locally.":[64],"For":[65],"situations":[66],"where":[67],"lower":[73],"than":[75],"entirely":[79],"client,":[83],"improves":[85],"pre-trained":[90],"weights":[92],"be":[94],"similar":[95],"or":[97],"exceed":[98],"those":[99],"client":[103],"This":[105],"paper":[106],"evaluates":[107],"two":[108],"(FFL)":[111],"algorithms":[112],"as":[113,152],"starting":[114],"points":[115],"for":[116,141,174,214],"personalisation.":[117],"Our":[118,162],"results":[119],"show":[120],"that":[121,155,196],"FFL":[122],"no":[124],"in":[129,168,185],"language":[131,176],"task":[132,177,188],"and":[133,209,217],"may":[134,198],"double":[135],"number":[137,170,181],"underperforming":[139,172,183],"image":[143,187],"task.":[144],"Instead,":[145],"we":[146],"propose":[147],"Personalisation-aware":[148],"(PaFL)":[151],"paradigm":[154],"pre-emptively":[156],"uses":[157],"during":[160],"training.":[161],"technique":[163],"shows":[164],"50%":[166],"reduction":[167],"lowering":[179],"instead":[189],"doubling":[191],"it.":[192],"Thus,":[193],"evidence":[194],"indicates":[195],"it":[197],"allow":[199],"broader":[201],"set":[202],"devices":[204],"represents":[210],"promising":[212],"avenue":[213],"future":[215],"experimentation":[216],"theoretical":[218],"analysis.":[219]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
