{"id":"https://openalex.org/W7164211324","doi":"https://doi.org/10.48550/arxiv.2606.10124","title":"FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching","display_name":"FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7164211324","doi":"https://doi.org/10.48550/arxiv.2606.10124"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10124","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10124","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.10124","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138386090","display_name":"Haoran Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haoran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084234133","display_name":"Cain\u00e3 Figueiredo Pereira","orcid":"https://orcid.org/0000-0003-0836-9681"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pereira, Cain\u00e3 Figueiredo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039796708","display_name":"Marie Siew","orcid":"https://orcid.org/0000-0002-9764-5010"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siew, Marie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138380325","display_name":"Xutong Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xutong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138359487","display_name":"Carlee Joe-Wong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joe-Wong, Carlee","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5078537181","display_name":"Rachid El-Azouzi","orcid":"https://orcid.org/0000-0002-4756-0887"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"El-Azouzi, Rachid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.3009999990463257,"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":0.3009999990463257,"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/T11478","display_name":"Caching and Content Delivery","score":0.1324000060558319,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.08320000022649765,"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/subspace-topology","display_name":"Subspace topology","score":0.7749999761581421},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.6726999878883362},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6362000107765198},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6171000003814697},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6152999997138977},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5206999778747559},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.5080000162124634}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8241999745368958},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.7749999761581421},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.6726999878883362},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6362000107765198},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6171000003814697},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6152999997138977},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5206999778747559},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.5080000162124634},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5048999786376953},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3880999982357025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36059999465942383},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3109999895095825},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.29809999465942383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2784999907016754},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10124","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10124","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.10124","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10124","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Federated":[0],"learning":[1],"(FL)":[2],"is":[3,26,130],"often":[4],"subject":[5],"to":[6,30,49,73,94,143],"aggregation":[7],"variance":[8],"if":[9],"clients":[10,25],"do":[11],"not":[12],"consistently":[13],"participate":[14],"in":[15,160,170],"training":[16],"rounds.":[17],"While":[18],"reusing":[19],"stale":[20],"model":[21],"updates":[22],"from":[23,66],"inactive":[24,103],"a":[27,58,63,67,76,96,133,139],"common":[28],"technique":[29],"reduce":[31],"this":[32,92],"variance,":[33],"we":[34,55],"find":[35,95],"that":[36,61,137,152],"with":[37,109],"skewed":[38],"client":[39,71,141],"participation,":[40],"the":[41,80,110,125,145],"resulting":[42],"update":[43],"staleness":[44],"can":[45],"become":[46],"severe":[47],"enough":[48],"destabilize":[50],"training.":[51],"To":[52],"remedy":[53],"this,":[54],"propose":[56],"FedSteer,":[57],"novel":[59],"method":[60],"constructs":[62],"gradient":[64,90],"subspace":[65,93,112],"cache":[68],"of":[69,79,98,167],"recent":[70],"gradients":[72,123],"serve":[74],"as":[75],"low-dimensional":[77],"representation":[78],"current":[81,126],"optimization":[82],"landscape.":[83],"FedSteer":[84,105,153],"projects":[85],"an":[86,102],"active":[87,116],"client's":[88],"true":[89],"onto":[91],"set":[97],"optimal":[99],"coordinates.":[100],"For":[101],"client,":[104],"reuses":[106],"these":[107],"coordinates":[108],"now-evolved":[111],"drifted":[113],"by":[114,132],"other":[115],"clients.":[117],"This":[118,129],"process":[119],"effectively":[120],"\"steers\"":[121],"outdated":[122],"toward":[124],"global":[127],"objective.":[128],"complemented":[131],"selective":[134],"caching":[135],"strategy":[136],"identifies":[138],"representative":[140],"subset":[142],"form":[144],"subspace,":[146],"reducing":[147],"server":[148],"memory.":[149],"Experiments":[150],"demonstrate":[151],"significantly":[154],"outperforms":[155],"baselines,":[156],"preventing":[157],"performance":[158],"collapse":[159],"challenging":[161],"scenarios":[162],"while":[163],"delivering":[164],"accuracy":[165],"gains":[166],"over":[168],"7%":[169],"others.":[171]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-11T00:00:00"}
