{"id":"https://openalex.org/W4403937544","doi":"https://doi.org/10.1109/iscc61673.2024.10733603","title":"FedDyS: Enhancing Federated Learning Efficiency with Dynamic Sample Selection","display_name":"FedDyS: Enhancing Federated Learning Efficiency with Dynamic Sample Selection","publication_year":2024,"publication_date":"2024-06-26","ids":{"openalex":"https://openalex.org/W4403937544","doi":"https://doi.org/10.1109/iscc61673.2024.10733603"},"language":"en","primary_location":{"id":"doi:10.1109/iscc61673.2024.10733603","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc61673.2024.10733603","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Symposium on Computers and Communications (ISCC)","raw_type":"proceedings-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/A5012983089","display_name":"Ensiye Kiyamousavi","orcid":"https://orcid.org/0009-0009-9700-8351"},"institutions":[{"id":"https://openalex.org/I58918642","display_name":"Sofia University \"St. Kliment Ohridski\"","ror":"https://ror.org/02jv3k292","country_code":"BG","type":"education","lineage":["https://openalex.org/I58918642"]}],"countries":["BG"],"is_corresponding":true,"raw_author_name":"S. Ensiye Kiyamousavi","raw_affiliation_strings":["Sofia University St. Kliment Ohridski,GATE Institute,Sofia,Bulgaria"],"affiliations":[{"raw_affiliation_string":"Sofia University St. Kliment Ohridski,GATE Institute,Sofia,Bulgaria","institution_ids":["https://openalex.org/I58918642"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051554470","display_name":"Boris Kraychev","orcid":"https://orcid.org/0009-0002-5217-9340"},"institutions":[{"id":"https://openalex.org/I58918642","display_name":"Sofia University \"St. Kliment Ohridski\"","ror":"https://ror.org/02jv3k292","country_code":"BG","type":"education","lineage":["https://openalex.org/I58918642"]}],"countries":["BG"],"is_corresponding":false,"raw_author_name":"Boris Kraychev","raw_affiliation_strings":["Sofia University St. Kliment Ohridski,GATE Institute,Sofia,Bulgaria"],"affiliations":[{"raw_affiliation_string":"Sofia University St. Kliment Ohridski,GATE Institute,Sofia,Bulgaria","institution_ids":["https://openalex.org/I58918642"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076908482","display_name":"Ivan Koychev","orcid":"https://orcid.org/0000-0003-3919-030X"},"institutions":[{"id":"https://openalex.org/I58918642","display_name":"Sofia University \"St. Kliment Ohridski\"","ror":"https://ror.org/02jv3k292","country_code":"BG","type":"education","lineage":["https://openalex.org/I58918642"]}],"countries":["BG"],"is_corresponding":false,"raw_author_name":"Ivan Koychev","raw_affiliation_strings":["Sofia University St. Kliment Ohridski,FMI,Sofia,Bulgaria"],"affiliations":[{"raw_affiliation_string":"Sofia University St. Kliment Ohridski,FMI,Sofia,Bulgaria","institution_ids":["https://openalex.org/I58918642"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5012983089"],"corresponding_institution_ids":["https://openalex.org/I58918642"],"apc_list":null,"apc_paid":null,"fwci":0.3626,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.68327603,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9714000225067139,"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":0.9714000225067139,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9397000074386597,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9289000034332275,"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/computer-science","display_name":"Computer science","score":0.7209242582321167},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.687110424041748},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6135027408599854},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37485307455062866}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7209242582321167},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.687110424041748},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6135027408599854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37485307455062866},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscc61673.2024.10733603","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc61673.2024.10733603","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Symposium on Computers and Communications (ISCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2083551746","https://openalex.org/W2535838896","https://openalex.org/W2912213068","https://openalex.org/W2963163009","https://openalex.org/W2963350250","https://openalex.org/W2989289980","https://openalex.org/W3016818045","https://openalex.org/W3021654819","https://openalex.org/W3043758338","https://openalex.org/W3045674654","https://openalex.org/W3045889854","https://openalex.org/W3154335119","https://openalex.org/W3156310591","https://openalex.org/W3159623990","https://openalex.org/W3167424384","https://openalex.org/W3200840849","https://openalex.org/W3204012185","https://openalex.org/W3207378505","https://openalex.org/W4283032505","https://openalex.org/W4283795007","https://openalex.org/W4285207539","https://openalex.org/W4287332481","https://openalex.org/W4313325044","https://openalex.org/W4385767680","https://openalex.org/W6685644109","https://openalex.org/W6685663092","https://openalex.org/W6687681856","https://openalex.org/W6728757088","https://openalex.org/W6738383168","https://openalex.org/W6749892895","https://openalex.org/W6752029299","https://openalex.org/W6759226220","https://openalex.org/W6759238902","https://openalex.org/W6768537144","https://openalex.org/W6772307254"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Federated":[0],"learning":[1],"(FL)":[2],"trains":[3],"models":[4],"across":[5],"multiple":[6],"devices":[7,39,94],"while":[8],"keeping":[9],"data":[10,21,25,77,98,106],"localized,":[11],"addressing":[12],"privacy":[13],"and":[14,54,75,104,131],"efficiency":[15],"issues.":[16],"It":[17],"faces":[18],"challenges":[19],"like":[20,40],"heterogeneity-where":[22],"diverse":[23],"local":[24,93],"might":[26],"hinder":[27],"the":[28,88,111,139],"performance":[29],"of":[30,138,142,152],"a":[31,66,115],"global":[32],"model-and":[33],"high":[34],"computational":[35,73],"costs":[36],"on":[37,92],"resource-limited":[38],"smartphones":[41],"or":[42],"IoT":[43],"sensors.":[44],"These":[45],"constraints":[46],"can":[47],"prolong":[48],"training":[49,82,89,103],"times,":[50],"increase":[51],"energy":[52],"use,":[53],"hasten":[55],"device":[56],"wear.":[57],"To":[58],"address":[59],"these":[60],"issues,":[61],"this":[62],"paper":[63],"introduces":[64],"FedDyS,":[65],"dynamic":[67],"sample":[68],"selection":[69],"technique":[70],"that":[71,123],"reduces":[72],"demands":[74],"mitigates":[76],"heterogeneity":[78],"by":[79],"eliminating":[80],"non-essential":[81],"samples.":[83],"This":[84],"not":[85],"only":[86],"shrinks":[87],"set":[90],"size":[91],"but":[95],"also":[96],"enhances":[97],"diversity,":[99],"facilitating":[100],"more":[101],"efficient":[102],"preserving":[105],"privacy.":[107],"Additionally,":[108],"FedDyS":[109,124,153],"prevents":[110],"catastrophic":[112],"forgetting":[113],"effect,":[114],"common":[116],"challenge":[117],"in":[118,129],"FL.":[119],"Our":[120],"experiments":[121],"show":[122],"surpasses":[125],"traditional":[126],"FL":[127],"methods":[128],"accuracy":[130],"convergence":[132],"speed,":[133],"using":[134],"less":[135],"than":[136],"15%":[137],"usual":[140],"number":[141],"samples,":[143],"making":[144],"it":[145],"ideal":[146],"for":[147],"low-resource":[148],"settings.":[149],"The":[150],"code":[151],"is":[154],"publicly":[155],"available:":[156],"https://github.com/ensiyeKiya/FedDyS":[157]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}