{"id":"https://openalex.org/W4405975153","doi":"https://doi.org/10.1109/pimrc59610.2024.10817436","title":"Privacy-Preserving Federated Learning for Coverage Prediction","display_name":"Privacy-Preserving Federated Learning for Coverage Prediction","publication_year":2024,"publication_date":"2024-09-02","ids":{"openalex":"https://openalex.org/W4405975153","doi":"https://doi.org/10.1109/pimrc59610.2024.10817436"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc59610.2024.10817436","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc59610.2024.10817436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","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/A5002812686","display_name":"Congyu Fang","orcid":"https://orcid.org/0000-0001-8890-7843"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]},{"id":"https://openalex.org/I4210127509","display_name":"Vector Institute","ror":"https://ror.org/03kqdja62","country_code":"CA","type":"facility","lineage":["https://openalex.org/I4210127509"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Congyu Fang","raw_affiliation_strings":["University of Toronto Vector Institute"],"affiliations":[{"raw_affiliation_string":"University of Toronto Vector Institute","institution_ids":["https://openalex.org/I4210127509","https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074645771","display_name":"Akram Bin Sediq","orcid":"https://orcid.org/0000-0003-1260-2853"},"institutions":[{"id":"https://openalex.org/I4210094041","display_name":"Ericsson (Canada)","ror":"https://ror.org/00nas2c56","country_code":"CA","type":"company","lineage":["https://openalex.org/I1306339040","https://openalex.org/I4210094041"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Akram Bin Sediq","raw_affiliation_strings":["Ericsson Canada Inc"],"affiliations":[{"raw_affiliation_string":"Ericsson Canada Inc","institution_ids":["https://openalex.org/I4210094041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031532274","display_name":"Hamza \u00dcmit S\u00f6k\u00fcn","orcid":"https://orcid.org/0000-0002-9785-3530"},"institutions":[{"id":"https://openalex.org/I4210094041","display_name":"Ericsson (Canada)","ror":"https://ror.org/00nas2c56","country_code":"CA","type":"company","lineage":["https://openalex.org/I1306339040","https://openalex.org/I4210094041"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hamza Sokun","raw_affiliation_strings":["Ericsson Canada Inc"],"affiliations":[{"raw_affiliation_string":"Ericsson Canada Inc","institution_ids":["https://openalex.org/I4210094041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049302162","display_name":"Israfil Bahceci","orcid":"https://orcid.org/0000-0002-2826-6885"},"institutions":[{"id":"https://openalex.org/I4210094041","display_name":"Ericsson (Canada)","ror":"https://ror.org/00nas2c56","country_code":"CA","type":"company","lineage":["https://openalex.org/I1306339040","https://openalex.org/I4210094041"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Israfil Bahceci","raw_affiliation_strings":["Ericsson Canada Inc"],"affiliations":[{"raw_affiliation_string":"Ericsson Canada Inc","institution_ids":["https://openalex.org/I4210094041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073843430","display_name":"Anas Ibrahim","orcid":"https://orcid.org/0000-0001-6278-2165"},"institutions":[{"id":"https://openalex.org/I4210094041","display_name":"Ericsson (Canada)","ror":"https://ror.org/00nas2c56","country_code":"CA","type":"company","lineage":["https://openalex.org/I1306339040","https://openalex.org/I4210094041"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"A Ahmed Ibrahim","raw_affiliation_strings":["Ericsson Canada Inc"],"affiliations":[{"raw_affiliation_string":"Ericsson Canada Inc","institution_ids":["https://openalex.org/I4210094041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018809423","display_name":"Nicolas Papernot","orcid":"https://orcid.org/0000-0001-5078-7233"},"institutions":[{"id":"https://openalex.org/I4210127509","display_name":"Vector Institute","ror":"https://ror.org/03kqdja62","country_code":"CA","type":"facility","lineage":["https://openalex.org/I4210127509"]},{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Nicolas Papernot","raw_affiliation_strings":["University of Toronto Vector Institute"],"affiliations":[{"raw_affiliation_string":"University of Toronto Vector Institute","institution_ids":["https://openalex.org/I4210127509","https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5002812686"],"corresponding_institution_ids":["https://openalex.org/I185261750","https://openalex.org/I4210127509"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22852321,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9172000288963318,"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/T12034","display_name":"Digital and Cyber Forensics","score":0.9007999897003174,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.7665780782699585},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.47506994009017944},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4525564908981323},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3852527141571045},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.3694714903831482},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.33540207147598267},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2816199064254761}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7665780782699585},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.47506994009017944},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4525564908981323},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3852527141571045},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.3694714903831482},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.33540207147598267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2816199064254761}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/pimrc59610.2024.10817436","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc59610.2024.10817436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","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":18,"referenced_works":["https://openalex.org/W2027595342","https://openalex.org/W2473418344","https://openalex.org/W2535690855","https://openalex.org/W2594311007","https://openalex.org/W2807584257","https://openalex.org/W2907414710","https://openalex.org/W2918305131","https://openalex.org/W3167616353","https://openalex.org/W4207048463","https://openalex.org/W4288057780","https://openalex.org/W4385412495","https://openalex.org/W6728757088","https://openalex.org/W6746720608","https://openalex.org/W6747855403","https://openalex.org/W6752985224","https://openalex.org/W6766757622","https://openalex.org/W6791899593","https://openalex.org/W6980800657"],"related_works":["https://openalex.org/W2584827882","https://openalex.org/W3195097297","https://openalex.org/W4225340788","https://openalex.org/W3038106605","https://openalex.org/W2513267613","https://openalex.org/W3049084372","https://openalex.org/W2528109871","https://openalex.org/W2940702331","https://openalex.org/W2905822832","https://openalex.org/W2240244939"],"abstract_inverted_index":{"In":[0,41,122],"5G":[1],"cellular":[2,30],"networks,":[3],"Machine":[4],"Learning":[5,55],"(ML)":[6],"can":[7,113],"be":[8,178],"exploited":[9],"to":[10,129,139,167],"predict":[11],"if":[12],"a":[13,23,52,97,145,179],"user":[14],"equipment":[15],"(UE)":[16],"is":[17,74],"in":[18,51,92,100,189],"the":[19,46,60,71,78,83,86,107,131,135,140,171],"coverage":[20,152],"area":[21],"of":[22,48,88,102],"neighbouring":[24],"cell.":[25],"This":[26],"could":[27,177],"improve":[28],"crucial":[29],"network":[31],"functionalities,":[32],"such":[33],"as":[34],"handovers,":[35],"interference":[36],"mitigation":[37],"and":[38,96,192],"carrier":[39],"aggregation.":[40],"this":[42,69,175],"paper,":[43],"we":[44,124,173],"study":[45,130],"enhancement":[47],"UEs\u2019":[49],"privacy":[50,73,95,137,165],"Differentially":[53],"Private-Federated":[54],"(DP-FL)":[56],"scheme":[57],"relying":[58],"on":[59],"sampled":[61],"Gaussian":[62],"mechanism,":[63],"assuming":[64],"honest-but-curious":[65],"threat":[66],"model.":[67],"With":[68],"technique,":[70],"UE\u2019s":[72],"protected":[75],"by":[76],"perturbing":[77],"averaged":[79],"updates":[80],"conducted":[81],"at":[82],"server;":[84],"also,":[85],"usage":[87],"client":[89],"subsampling":[90],"results":[91],"an":[93],"amplified":[94],"reduced":[98],"overhead":[99],"terms":[101],"communication.":[103],"We":[104,143],"demonstrate":[105],"that":[106,133,148,150],"models":[108,160],"trained":[109],"with":[110],"our":[111],"approach":[112],"achieve":[114],"better":[115],"privacy-utility":[116],"tradeoff":[117],"than":[118],"previous":[119],"works":[120],"can.":[121],"addition,":[123],"conduct":[125],"membership":[126],"inference":[127],"attack":[128],"factors":[132],"impact":[134],"empirical":[136,164],"protection":[138,166],"training":[141,168],"data.":[142,169],"make":[144],"novel":[146],"observation":[147,176],"suggests":[149],"for":[151,182],"prediction":[153],"task,":[154],"larger":[155],"datasets":[156],"and/or":[157],"smaller":[158],"ML":[159],"would":[161],"provide":[162],"stronger":[163],"Beyond":[170],"task":[172],"consider,":[174],"useful":[180],"insight":[181],"dataset":[183],"curation":[184],"or":[185],"model":[186],"architecture":[187],"selection":[188],"other":[190],"domains":[191],"warrants":[193],"additional":[194],"investigation.":[195]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}