{"id":"https://openalex.org/W7162672382","doi":"https://doi.org/10.48550/arxiv.2605.28335","title":"Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee","display_name":"Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162672382","doi":"https://doi.org/10.48550/arxiv.2605.28335"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.28335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28335","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.2605.28335","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039016289","display_name":"Shiyuan Zuo","orcid":"https://orcid.org/0009-0001-5892-4834"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zuo, Shiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137290559","display_name":"Jiashuo Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jiashuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137268979","display_name":"Rongfei Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Rongfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137276223","display_name":"Han Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137259674","display_name":"Jie Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jie","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":0.8492000102996826,"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":0.8492000102996826,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.06210000067949295,"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":0.05550000071525574,"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":0.5913000106811523},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5559999942779541},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5479999780654907},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5461999773979187},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5365999937057495},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4596000015735626},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4406999945640564},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.435699999332428},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.43560001254081726}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.753000020980835},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5913000106811523},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5559999942779541},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5479999780654907},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5461999773979187},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5432999730110168},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5365999937057495},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4596000015735626},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4406999945640564},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.435699999332428},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C158968445","wikidata":"https://www.wikidata.org/wiki/Q7631150","display_name":"Subgradient method","level":2,"score":0.41040000319480896},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4034000039100647},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35659998655319214},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.328000009059906},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.27619999647140503},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26489999890327454},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.2565000057220459},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.28335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28335","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.2605.28335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28335","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":{"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],"multiple":[4],"clients":[5,119],"to":[6,19,93,105,135],"collaboratively":[7],"train":[8],"models":[9,54],"without":[10],"sharing":[11],"raw":[12],"data,":[13],"but":[14,29],"it":[15],"is":[16,115,124],"highly":[17,240],"vulnerable":[18],"Byzantine":[20,206],"attacks.":[21],"Existing":[22],"robust":[23,75,79],"approaches":[24],"can":[25],"neutralize":[26],"these":[27],"threats":[28],"incur":[30],"substantial":[31],"computational":[32,60,103],"overhead":[33,39],"during":[34],"high-dimensional":[35],"gradient":[36],"aggregation,":[37],"an":[38,106],"that":[40,160,194,224],"scales":[41],"poorly":[42],"with":[43,227],"model":[44,126],"size":[45],"and":[46,120,173],"increasingly":[47],"dominates":[48],"the":[49,101,116,125,129,137,154,186,204],"training":[50],"cost":[51],"as":[52],"modern":[53],"grow":[55],"larger.":[56],"To":[57],"address":[58],"this":[59,195],"bottleneck,":[61],"we":[62,158,191],"propose":[63],"Projected":[64],"Dimensionality":[65],"Reduction":[66],"(PDR),":[67],"a":[68,85,210],"universal":[69],"acceleration":[70,197],"framework":[71],"for":[72,170,177,200],"vector-level":[73],"distance-based":[74],"aggregators,":[76],"which":[77],"performs":[78],"aggregation":[80],"by":[81,209],"compressing":[82],"gradients":[83],"into":[84],"drastically":[86],"smaller":[87],"subspace":[88],"via":[89],"sparse":[90],"random":[91],"projection":[92],"efficiently":[94],"compute":[95],"reliability":[96],"weights.":[97],"This":[98],"approach":[99],"reduces":[100],"server":[102],"complexity":[104],"optimal":[107,163],"$":[108,112,114,121,123,167,169,174,176,182,184,215],"\\mathcal{O}(Mp)":[109],"$,":[110],"where":[111,181],"M":[113],"number":[117,187],"of":[118,166,188,214,232],"p":[122],"dimension,":[127],"matching":[128],"theoretical":[130],"lower":[131],"bound":[132],"required":[133],"merely":[134,202],"read":[136],"gradients.":[138],"We":[139],"establish":[140],"convergence":[141,164,242],"guarantees":[142],"under":[143],"standard":[144],"FL":[145,150],"assumptions":[146],"in":[147,235],"prior":[148],"Byzantine-robust":[149],"analyses.":[151],"By":[152],"leveraging":[153],"Subspace":[155],"Embedding":[156],"Theorem,":[157],"show":[159],"PDR":[161,226],"achieves":[162],"rates":[165],"\\mathcal{O}(1/\\sqrt{T})":[168],"non-convex":[171],"functions":[172],"\\mathcal{O}(1/T)":[175],"strongly":[178],"convex":[179],"functions,":[180],"T":[183],"denotes":[185],"iterations.":[189],"Crucially,":[190],"mathematically":[192],"demonstrate":[193],"massive":[196],"comes":[198],"almost":[199],"free,":[201],"inflating":[203],"inherent":[205],"error":[207],"floor":[208],"bounded,":[211],"tunable":[212],"factor":[213],"\\frac{1+\u03b5}{1-\u03b5}":[216],"$.":[217],"Experimental":[218],"results":[219],"on":[220],"benchmark":[221],"datasets":[222],"confirm":[223],"integrating":[225],"existing":[228],"aggregators":[229],"yields":[230],"orders":[231],"magnitude":[233],"speedups":[234],"time":[236],"efficiency":[237],"while":[238],"maintaining":[239],"competitive":[241],"performance.":[243]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-29T00:00:00"}