{"id":"https://openalex.org/W7167785008","doi":"https://doi.org/10.48550/arxiv.2607.06979","title":"Robust Federated Learning Under Real-World Client Churn","display_name":"Robust Federated Learning Under Real-World Client Churn","publication_year":2026,"publication_date":"2026-07-08","ids":{"openalex":"https://openalex.org/W7167785008","doi":"https://doi.org/10.48550/arxiv.2607.06979"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.06979","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.06979","pdf_url":null,"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":"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.2607.06979","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102962728","display_name":"Dhruv Garg","orcid":"https://orcid.org/0009-0002-7655-845X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garg, Dhruv","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062670367","display_name":"Neha Lakhani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lakhani, Neha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023862266","display_name":"Debopam Sanyal","orcid":"https://orcid.org/0000-0002-6761-1389"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sanyal, Debopam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140373386","display_name":"Myungjin Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Myungjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048451114","display_name":"Alexey Tumanov","orcid":"https://orcid.org/0009-0005-7862-1477"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tumanov, Alexey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5085918364","display_name":"Ada Gavrilovska","orcid":"https://orcid.org/0000-0003-4199-2512"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gavrilovska, Ada","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":0.6815999746322632,"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":0.6815999746322632,"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/T13553","display_name":"Age of Information Optimization","score":0.03539999946951866,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.02449999935925007,"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/leverage","display_name":"Leverage (statistics)","score":0.7461000084877014},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.720300018787384},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6220999956130981},{"id":"https://openalex.org/keywords/orchestration","display_name":"Orchestration","score":0.4406999945640564},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.42660000920295715},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3677000105381012},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.3637999892234802},{"id":"https://openalex.org/keywords/client-side","display_name":"Client-side","score":0.3138999938964844}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.861299991607666},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7461000084877014},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.720300018787384},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6220999956130981},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5271000266075134},{"id":"https://openalex.org/C199168358","wikidata":"https://www.wikidata.org/wiki/Q3367000","display_name":"Orchestration","level":3,"score":0.4406999945640564},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.3637999892234802},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.31690001487731934},{"id":"https://openalex.org/C202477664","wikidata":"https://www.wikidata.org/wiki/Q1352449","display_name":"Client-side","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.2793999910354614},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2687999904155731},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C65813073","wikidata":"https://www.wikidata.org/wiki/Q1622420","display_name":"High availability","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C2779019669","wikidata":"https://www.wikidata.org/wiki/Q25203946","display_name":"Asynchrony (computer programming)","level":3,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.06979","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.06979","pdf_url":null,"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":"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.2607.06979","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.06979","pdf_url":null,"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":"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"enables":[3],"training":[4],"shared":[5],"models":[6,63],"on":[7,117,184],"private,":[8],"on-device":[9],"data,":[10],"but":[11],"production":[12],"deployments":[13],"remain":[14],"constrained":[15],"to":[16,22,45,48,67,133,153,213,222],"slow,":[17],"multi-day":[18],"refresh":[19,156],"cycles":[20],"due":[21],"the":[23,43,173],"complexity":[24],"of":[25,188],"coordinating":[26],"massive":[27],"client":[28,92,189],"populations.":[29],"For":[30],"applications":[31],"such":[32],"as":[33],"feed":[34],"ranking,":[35],"ad":[36],"targeting,":[37],"and":[38,65,75,97,102,158,202,225],"personalized":[39],"recommendation,":[40],"model":[41,100,175],"freshness:":[42],"ability":[44],"rapidly":[46],"adapt":[47],"new":[49],"user-local":[50],"data":[51,69,95],"is":[52],"critical":[53],"for":[54],"maximizing":[55],"objectives":[56],"like":[57],"click-through":[58],"rate.":[59],"This":[60],"lag":[61],"leaves":[62],"stale":[64,177],"unresponsive":[66],"volatile":[68],"distributions":[70],"driven":[71],"by":[72,87,211,219],"viral":[73],"trends":[74],"shifting":[76],"user":[77],"intent.":[78],"Bridging":[79],"this":[80],"gap":[81],"requires":[82],"addressing":[83],"three":[84,123],"challenges":[85],"overlooked":[86],"existing":[88],"FL":[89,109,227],"systems:":[90],"transient":[91],"availability,":[93,190],"dynamic":[94],"heterogeneity,":[96],"delays":[98],"between":[99],"predictions":[101],"observable":[103],"outcomes.":[104],"We":[105],"present":[106],"FeLiX,":[107],"an":[108],"orchestration":[110],"framework":[111],"that":[112,129,145,163,182],"minimizes":[113],"wall-clock":[114,208],"time-to-target":[115,209],"accuracy":[116,210],"live":[118],"interaction":[119],"streams.":[120],"FeLiX":[121,191,206],"introduces":[122],"primitives:":[124],"(i)":[125],"streaming-aware":[126],"availability":[127],"tiers":[128],"leverage":[130],"lightweight":[131],"telemetry":[132],"identify":[134],"ready":[135],"clients":[136],"at":[137],"scale;":[138],"(ii)":[139],"fresh-utility":[140],"selection,":[141],"a":[142],"dual-tier":[143],"mechanism":[144],"prioritizes":[146],"statistically":[147],"valuable":[148],"updates":[149,167],"from":[150],"devices":[151],"able":[152],"meet":[154],"tight":[155],"deadlines;":[157],"(iii)":[159],"informativeness-aware,":[160],"delay-robust":[161],"aggregation":[162],"incorporates":[164],"late,":[165],"high-value":[166],"containing":[168],"ground-truth":[169],"outcomes":[170],"without":[171],"biasing":[172],"global":[174],"toward":[176],"distributions.":[178],"Unlike":[179],"prior":[180],"systems":[181],"rely":[183],"unrealistic":[185],"oracular":[186],"knowledge":[187],"achieves":[192],"near-oracular":[193],"performance":[194],"in":[195],"real-world":[196],"settings.":[197],"Across":[198],"CIFAR-10,":[199],"Google":[200],"Speech,":[201],"realistic":[203],"low-availability":[204],"traces,":[205],"reduces":[207],"up":[212],"2.37X":[214],"while":[215],"reducing":[216],"communication":[217],"bandwidth":[218],"1.30X":[220],"compared":[221],"state-of-the-art":[223],"synchronous":[224],"asynchronous":[226],"baselines.":[228]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-10T00:00:00"}
