{"id":"https://openalex.org/W7128490745","doi":"https://doi.org/10.48550/arxiv.2602.08617","title":"ERIS: Enhancing Privacy and Scalability in Federated Learning via Federated Shard Aggregation","display_name":"ERIS: Enhancing Privacy and Scalability in Federated Learning via Federated Shard Aggregation","publication_year":2026,"publication_date":"2026-02-09","ids":{"openalex":"https://openalex.org/W7128490745","doi":"https://doi.org/10.48550/arxiv.2602.08617"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.08617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.08617","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.2602.08617","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103085621","display_name":"Dario Fenoglio","orcid":"https://orcid.org/0009-0002-9112-9522"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fenoglio, Dario","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117629956","display_name":"Pasquale Polverino","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Polverino, Pasquale","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093949546","display_name":"Jacopo Quizi","orcid":"https://orcid.org/0009-0001-9199-2812"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quizi, Jacopo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037458079","display_name":"Martin Gjoreski","orcid":"https://orcid.org/0000-0002-1220-7418"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gjoreski, Martin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5146794735","display_name":"Akash Dhasade","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dhasade, Akash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5069422623","display_name":"Marc Langheinrich","orcid":"https://orcid.org/0000-0002-8834-7388"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Langheinrich, Marc","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":0.9132000207901001,"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":0.9132000207901001,"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/T10237","display_name":"Cryptography and Data Security","score":0.03590000048279762,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.010499999858438969,"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/bottleneck","display_name":"Bottleneck","score":0.6883000135421753},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5764999985694885},{"id":"https://openalex.org/keywords/cryptography","display_name":"Cryptography","score":0.5289999842643738},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42559999227523804},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.414900004863739},{"id":"https://openalex.org/keywords/secure-communication","display_name":"Secure communication","score":0.3720000088214874},{"id":"https://openalex.org/keywords/information-leakage","display_name":"Information leakage","score":0.3560999929904938},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.34850001335144043},{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.3334999978542328}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8370000123977661},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6883000135421753},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5764999985694885},{"id":"https://openalex.org/C178489894","wikidata":"https://www.wikidata.org/wiki/Q8789","display_name":"Cryptography","level":2,"score":0.5289999842643738},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.4544000029563904},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.37619999051094055},{"id":"https://openalex.org/C510870077","wikidata":"https://www.wikidata.org/wiki/Q7444868","display_name":"Secure communication","level":3,"score":0.3720000088214874},{"id":"https://openalex.org/C2779201187","wikidata":"https://www.wikidata.org/wiki/Q2775060","display_name":"Information leakage","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33559998869895935},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3151000142097473},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C94284585","wikidata":"https://www.wikidata.org/wiki/Q228184","display_name":"Random oracle","level":4,"score":0.2985000014305115},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C158156997","wikidata":"https://www.wikidata.org/wiki/Q1416645","display_name":"Models of communication","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C137822555","wikidata":"https://www.wikidata.org/wiki/Q2587068","display_name":"Information sensitivity","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C15927051","wikidata":"https://www.wikidata.org/wiki/Q246593","display_name":"Cryptographic primitive","level":4,"score":0.27000001072883606},{"id":"https://openalex.org/C52622258","wikidata":"https://www.wikidata.org/wiki/Q131222","display_name":"Information theory","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.26269999146461487},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C101765175","wikidata":"https://www.wikidata.org/wiki/Q577764","display_name":"Communications system","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.08617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.08617","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.2602.08617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.08617","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":{"Scaling":[0],"Federated":[1,49],"Learning":[2],"(FL)":[3],"to":[4,82,103,171],"billion-parameter":[5],"models":[6],"forces":[7],"a":[8,53],"challenging":[9],"trade-off":[10],"between":[11],"privacy,":[12],"scalability,":[13],"and":[14,35,86,107,119,138,150,168,174],"model":[15],"utility.":[16],"Existing":[17],"solutions":[18],"often":[19],"tackle":[20],"these":[21],"challenges":[22],"in":[23],"isolation,":[24],"sacrificing":[25],"accuracy,":[26],"relying":[27,178],"on":[28,48,179],"costly":[29],"cryptographic":[30],"tools,":[31],"or":[32,182],"introducing":[33],"communication":[34,166],"optimization":[36],"inefficiencies":[37],"that":[38,56,112,158],"affect":[39],"convergence.":[40],"We":[41,110],"introduce":[42],"ERIS,":[43],"an":[44],"FL":[45,90],"framework":[46],"centered":[47],"Shard":[50],"Aggregation":[51],"(FSA),":[52],"novel":[54],"mechanism":[55],"partitions":[57],"each":[58,129],"client":[59],"update":[60,91],"into":[61],"non-overlapping":[62],"shards":[63],"whose":[64],"aggregation":[65,76],"is":[66,145],"distributed":[67],"across":[68,148],"multiple":[69],"client-side":[70,136],"aggregators.":[71],"FSA":[72],"removes":[73],"the":[74,79,88,125,133,140],"central":[75],"bottleneck,":[77],"limits":[78],"information":[80,122],"visible":[81],"any":[83],"single":[84],"observer,":[85],"preserves":[87,114],"centralized":[89],"after":[92],"reassembly.":[93],"ERIS":[94,113,159],"can":[95],"further":[96],"readily":[97],"integrate":[98],"Distributed":[99],"Shifted":[100],"Compression":[101],"(DSC)":[102],"reduce":[104],"transmitted":[105],"payloads":[106],"exposed":[108],"coordinates.":[109],"prove":[111],"convergence":[115],"under":[116],"standard":[117],"assumptions":[118],"bounds":[120],"mutual":[121],"leakage":[123],"by":[124],"observable":[126],"fraction":[127],"of":[128,135],"update,":[130],"decreasing":[131],"with":[132,139],"number":[134],"aggregators,":[137],"compression":[141],"level":[142],"when":[143],"DSC":[144],"enabled.":[146],"Experiments":[147],"image":[149],"text":[151],"tasks,":[152],"including":[153],"large":[154],"language":[155],"models,":[156],"show":[157],"achieves":[160],"FedAvg-level":[161],"utility":[162],"while":[163],"substantially":[164],"reducing":[165],"bottlenecks":[167],"improving":[169],"robustness":[170],"membership":[172],"inference":[173],"reconstruction":[175],"attacks,":[176],"without":[177],"heavy":[180],"cryptography":[181],"utility-degrading":[183],"perturbations.":[184]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-02-11T00:00:00"}