{"id":"https://openalex.org/W4375868952","doi":"https://doi.org/10.1109/icassp49357.2023.10095468","title":"Byzantine-Robust and Communication-Efficient Personalized Federated Learning","display_name":"Byzantine-Robust and Communication-Efficient Personalized Federated Learning","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4375868952","doi":"https://doi.org/10.1109/icassp49357.2023.10095468"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49357.2023.10095468","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10095468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100836384","display_name":"Xuechao He","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xuechao He","raw_affiliation_strings":["Sun Yat-Sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100747003","display_name":"Jiaojiao Zhang","orcid":"https://orcid.org/0000-0002-4611-9424"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Jiaojiao Zhang","raw_affiliation_strings":["KTH Royal Institute of Technology"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029820392","display_name":"Qing Ling","orcid":"https://orcid.org/0000-0003-4222-5964"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qing Ling","raw_affiliation_strings":["Sun Yat-Sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100836384"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3986,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.84500685,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9980999827384949,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9797999858856201,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.833287239074707},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.820520281791687},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8022406101226807},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.6775736212730408},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.5975170731544495},{"id":"https://openalex.org/keywords/byzantine-fault-tolerance","display_name":"Byzantine fault tolerance","score":0.5169848203659058},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.4715328812599182},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4646086096763611},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.44630852341651917},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4424819350242615},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4323646128177643},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42428526282310486},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.42096707224845886},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.418710857629776},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3474441468715668},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3167380094528198}],"concepts":[{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.833287239074707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.820520281791687},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8022406101226807},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.6775736212730408},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.5975170731544495},{"id":"https://openalex.org/C168021876","wikidata":"https://www.wikidata.org/wiki/Q1353446","display_name":"Byzantine fault tolerance","level":3,"score":0.5169848203659058},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.4715328812599182},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4646086096763611},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.44630852341651917},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4424819350242615},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4323646128177643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42428526282310486},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.42096707224845886},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.418710857629776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3474441468715668},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3167380094528198},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/icassp49357.2023.10095468","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10095468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W2410099853","https://openalex.org/W2535838896","https://openalex.org/W2769644379","https://openalex.org/W2789911054","https://openalex.org/W2897474720","https://openalex.org/W2937905591","https://openalex.org/W2963179579","https://openalex.org/W2963334472","https://openalex.org/W2963773265","https://openalex.org/W2998402482","https://openalex.org/W3005776401","https://openalex.org/W3015640161","https://openalex.org/W3036814563","https://openalex.org/W3092408317","https://openalex.org/W3133814152","https://openalex.org/W3137092842","https://openalex.org/W3194331388","https://openalex.org/W3213815372","https://openalex.org/W4213446860","https://openalex.org/W4221140101","https://openalex.org/W4224918489","https://openalex.org/W4225298562","https://openalex.org/W4285762978","https://openalex.org/W4287756313","https://openalex.org/W4297687186","https://openalex.org/W4318619660","https://openalex.org/W6728757088","https://openalex.org/W6746200960","https://openalex.org/W6748786018","https://openalex.org/W6751961731","https://openalex.org/W6752191696","https://openalex.org/W6754416507","https://openalex.org/W6755593096","https://openalex.org/W6773813173","https://openalex.org/W6779174293","https://openalex.org/W6780517973","https://openalex.org/W6784312123","https://openalex.org/W6790034021","https://openalex.org/W6802682914","https://openalex.org/W6809754270"],"related_works":["https://openalex.org/W4317941881","https://openalex.org/W4398164851","https://openalex.org/W3035996294","https://openalex.org/W4229067761","https://openalex.org/W4323521275","https://openalex.org/W3210293592","https://openalex.org/W4220738117","https://openalex.org/W4287755480","https://openalex.org/W3129381981","https://openalex.org/W2953763514"],"abstract_inverted_index":{"This":[0,29],"paper":[1],"investigates":[2],"personalized":[3,79,129],"federated":[4,130],"learning,":[5],"in":[6,22,77],"which":[7],"a":[8,15,25,60,78,97],"group":[9],"of":[10,38,84,114,121,140],"workers":[11,43,110],"are":[12],"coordinated":[13],"by":[14],"server":[16,90],"to":[17,24,49,67,111],"train":[18],"correlated":[19],"local":[20,94,115],"models,":[21],"addition":[23],"common":[26],"global":[27,86],"model.":[28],"distributed":[30],"statistical":[31],"learning":[32],"problem":[33],"faces":[34],"two":[35],"challenges:":[36],"efficiency":[37],"information":[39],"exchange":[40],"between":[41],"the":[42,45,54,69,82,85,89,93,108,122,141,145],"and":[44,47,88],"server,":[46],"robustness":[48,70],"potential":[50],"malicious":[51],"messages":[52],"from":[53],"so-called":[55],"Byzantine":[56],"workers.":[57],"We":[58],"propose":[59],"projected":[61],"stochastic":[62],"block":[63],"gradient":[64],"descent":[65,100],"method":[66,124,143],"address":[68],"issue.":[71],"Therein,":[72],"each":[73],"regular":[74,109],"worker":[75],"learns":[76],"manner":[80],"with":[81],"aid":[83],"model,":[87],"judiciously":[91],"aggregates":[92],"models":[95],"via":[96],"Huber":[98],"function-based":[99],"step.":[101],"To":[102],"improve":[103],"communication":[104,118],"efficiency,":[105],"we":[106],"allow":[107],"perform":[112],"multi-steps":[113],"update":[116],"per":[117],"round.":[119],"Convergence":[120],"proposed":[123,142],"is":[125],"established":[126],"for":[127],"non-convex":[128],"learning.":[131],"Numerical":[132],"experiments":[133],"on":[134],"neural":[135],"network":[136],"training":[137],"validate":[138],"advantages":[139],"over":[144],"existing":[146],"ones.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
