{"id":"https://openalex.org/W4416286097","doi":"https://doi.org/10.1109/lsp.2025.3633592","title":"Communication Efficient Over-the-Air Federated Learning With Random FLARE Algorithm","display_name":"Communication Efficient Over-the-Air Federated Learning With Random FLARE Algorithm","publication_year":2025,"publication_date":"2025-11-17","ids":{"openalex":"https://openalex.org/W4416286097","doi":"https://doi.org/10.1109/lsp.2025.3633592"},"language":null,"primary_location":{"id":"doi:10.1109/lsp.2025.3633592","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3633592","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081601806","display_name":"Young-Hun Seo","orcid":"https://orcid.org/0000-0003-3079-0843"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Younghun Seo","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science (EECS), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-3079-0843","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science (EECS), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hyuk Lim","orcid":"https://orcid.org/0000-0002-9926-3913"},"institutions":[{"id":"https://openalex.org/I4210087425","display_name":"Korea Energy Economics Institute","ror":"https://ror.org/0113xme87","country_code":"KR","type":"facility","lineage":["https://openalex.org/I4210087425","https://openalex.org/I4210097958"]},{"id":"https://openalex.org/I4210127102","display_name":"Korea Institute of Energy Research","ror":"https://ror.org/0298pes53","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210127102","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyuk Lim","raw_affiliation_strings":["Energy AI, Korea Institute of Energy Technology (KENTECH), Naju, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-9926-3913","affiliations":[{"raw_affiliation_string":"Energy AI, Korea Institute of Energy Technology (KENTECH), Naju, South Korea","institution_ids":["https://openalex.org/I4210087425","https://openalex.org/I4210127102"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054154402","display_name":"Nam Yul Yu","orcid":"https://orcid.org/0000-0001-9265-712X"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Nam Yul Yu","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science (EECS), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-9265-712X","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science (EECS), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea","institution_ids":["https://openalex.org/I39534123"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5081601806"],"corresponding_institution_ids":["https://openalex.org/I39534123"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17943662,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"171","last_page":"175"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9168000221252441,"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":0.9168000221252441,"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/T12676","display_name":"Machine Learning and ELM","score":0.010999999940395355,"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":0.009200000204145908,"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/upload","display_name":"Upload","score":0.7064999938011169},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6718999743461609},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5544000267982483},{"id":"https://openalex.org/keywords/compensation","display_name":"Compensation (psychology)","score":0.5271999835968018},{"id":"https://openalex.org/keywords/error-detection-and-correction","display_name":"Error detection and correction","score":0.4244999885559082},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.3815000057220459},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.3587000072002411},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.3531000018119812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7921000123023987},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.7064999938011169},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6718999743461609},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5932000279426575},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5544000267982483},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.5271999835968018},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.3815000057220459},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C3018824978","wikidata":"https://www.wikidata.org/wiki/Q2894891","display_name":"Error analysis","level":2,"score":0.3368000090122223},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3018999993801117},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C101722063","wikidata":"https://www.wikidata.org/wiki/Q218825","display_name":"Random access","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C2781020372","wikidata":"https://www.wikidata.org/wiki/Q533093","display_name":"On the fly","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2815999984741211},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26600000262260437},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C3020028006","wikidata":"https://www.wikidata.org/wiki/Q9158","display_name":"Electronic mail","level":2,"score":0.2578999996185303}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3633592","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3633592","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2002427601","https://openalex.org/W2194775991","https://openalex.org/W2405578611","https://openalex.org/W2606891064","https://openalex.org/W2907379776","https://openalex.org/W2991236681","https://openalex.org/W2999074226","https://openalex.org/W3018464563","https://openalex.org/W3153347425","https://openalex.org/W3157680283","https://openalex.org/W3167689670","https://openalex.org/W3188102017","https://openalex.org/W3193254256","https://openalex.org/W3217610115","https://openalex.org/W4206900626","https://openalex.org/W4286210752","https://openalex.org/W4286656549","https://openalex.org/W4297383547","https://openalex.org/W4300263211","https://openalex.org/W4312224516","https://openalex.org/W4312590601","https://openalex.org/W4313886867","https://openalex.org/W4386902918","https://openalex.org/W4391936064","https://openalex.org/W4393028667","https://openalex.org/W4396910150","https://openalex.org/W4402592942","https://openalex.org/W4404037594","https://openalex.org/W4412403832","https://openalex.org/W4412605348"],"related_works":[],"abstract_inverted_index":{"In":[0,27],"this":[1],"letter,":[2],"we":[3],"propose":[4],"a":[5,16,22,37,77],"communication":[6,46],"efficient":[7],"federated":[8],"learning":[9],"algorithm,":[10],"coined":[11],"random":[12,25],"FLARE":[13],"(R-FLARE),":[14],"using":[15,36,85,113],"novel":[17],"error":[18,73,92,124],"compensation":[19],"method":[20],"within":[21],"framework":[23],"of":[24,40,83],"sparsification.":[26],"the":[28,33,56,63,81,86,99,104,111],"R-FLARE,":[29],"all":[30],"devices":[31],"sparsify":[32],"local":[34,53,64],"gradients":[35],"common":[38],"set":[39],"randomly":[41],"selected":[42,57],"indices":[43],"to":[44,68],"improve":[45],"efficiency":[47],"with":[48],"over-the-air":[49],"computation.":[50],"To":[51],"upload":[52],"gradients,":[54],"only":[55],"gradient":[58],"elements":[59],"are":[60],"compensated":[61],"by":[62],"errors":[65],"accumulated":[66],"due":[67],"sparsification,":[69],"which":[70,94],"prevents":[71],"redundant":[72],"compensation.":[74],"We":[75],"conduct":[76],"theoretical":[78],"analysis":[79],"on":[80],"convergence":[82,101],"R-FLARE":[84,112],"<inline-formula":[87,114,119],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[88,115,120],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math":[89,116,121],"notation=\"LaTeX\">$l_{2}$</tex-math></inline-formula>":[90],"norm-based":[91],"compensation,":[93],"shows":[95],"that":[96,110],"it":[97],"achieves":[98],"same":[100],"rate":[102],"as":[103],"state-of-the-art":[105],"algorithms.":[106],"Numerical":[107],"results":[108],"show":[109],"notation=\"LaTeX\">$l_{1}$</tex-math></inline-formula>-":[117],"and":[118,132],"notation=\"LaTeX\">$l_{2}$</tex-math></inline-formula>-norm":[122],"based":[123],"compensations":[125],"outperform":[126],"conventional":[127],"algorithms":[128],"in":[129],"test":[130],"accuracy":[131],"training":[133],"speed.":[134]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-11-17T00:00:00"}
