{"id":"https://openalex.org/W4385568299","doi":"https://doi.org/10.1145/3580305.3599349","title":"FedSkill: Privacy Preserved Interpretable Skill Learning via Imitation","display_name":"FedSkill: Privacy Preserved Interpretable Skill Learning via Imitation","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385568299","doi":"https://doi.org/10.1145/3580305.3599349"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599349","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599349","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5006087820","display_name":"Yushan Jiang","orcid":"https://orcid.org/0000-0002-4226-7534"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yushan Jiang","raw_affiliation_strings":["University of Connecticut, Storrs, CT, USA"],"raw_orcid":"https://orcid.org/0000-0002-4226-7534","affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103210504","display_name":"Wenchao Yu","orcid":"https://orcid.org/0000-0002-2480-448X"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenchao Yu","raw_affiliation_strings":["NEC Labs America, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0002-2480-448X","affiliations":[{"raw_affiliation_string":"NEC Labs America, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013197657","display_name":"Dongjin Song","orcid":"https://orcid.org/0000-0002-7027-7916"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongjin Song","raw_affiliation_strings":["University of Connecticut, Storrs, CT, USA"],"raw_orcid":"https://orcid.org/0000-0002-7027-7916","affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083964854","display_name":"Lu Wang","orcid":"https://orcid.org/0000-0002-7305-1496"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lu Wang","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-7305-1496","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037724644","display_name":"Wei Cheng","orcid":"https://orcid.org/0000-0001-5456-626X"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Cheng","raw_affiliation_strings":["NEC Labs America, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0001-5456-626X","affiliations":[{"raw_affiliation_string":"NEC Labs America, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100456786","display_name":"Haifeng Chen","orcid":"https://orcid.org/0000-0002-9363-738X"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haifeng Chen","raw_affiliation_strings":["NEC Labs America, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0002-9363-738X","affiliations":[{"raw_affiliation_string":"NEC Labs America, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1010","last_page":"1019"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9961000084877014,"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":0.9961000084877014,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9904000163078308,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9621999859809875,"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/interpretability","display_name":"Interpretability","score":0.9328230023384094},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.759412407875061},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6427345275878906},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5895882844924927},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.5784907341003418},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.5310162901878357},{"id":"https://openalex.org/keywords/scarcity","display_name":"Scarcity","score":0.4665747582912445},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4459213614463806}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9328230023384094},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.759412407875061},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6427345275878906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5895882844924927},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.5784907341003418},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.5310162901878357},{"id":"https://openalex.org/C109747225","wikidata":"https://www.wikidata.org/wiki/Q815758","display_name":"Scarcity","level":2,"score":0.4665747582912445},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4459213614463806},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"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/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599349","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599349","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G435464297","display_name":"Collaborative Research: CNS CORE: Small: RUI: Hierarchical Deep Reinforcement Learning for Routing in Mobile Wireless Networks","funder_award_id":"2154191","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320307759","display_name":"General Electric","ror":"https://ror.org/013msgt25"},{"id":"https://openalex.org/F4320310017","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2031571562","https://openalex.org/W2098774185","https://openalex.org/W2174803659","https://openalex.org/W2187089797","https://openalex.org/W2280404143","https://openalex.org/W2396881363","https://openalex.org/W2962894046","https://openalex.org/W2964256412","https://openalex.org/W3008233947","https://openalex.org/W3012628688","https://openalex.org/W3034163621","https://openalex.org/W3035453001","https://openalex.org/W3038022836","https://openalex.org/W3080253043","https://openalex.org/W3091635927","https://openalex.org/W3133814152","https://openalex.org/W3185291728","https://openalex.org/W3193987867","https://openalex.org/W3209203357","https://openalex.org/W3213908012","https://openalex.org/W4213384663","https://openalex.org/W4240805545","https://openalex.org/W4283796083","https://openalex.org/W4285876308","https://openalex.org/W4286361939","https://openalex.org/W4286421857","https://openalex.org/W4307234290","https://openalex.org/W4324118822","https://openalex.org/W6759238902"],"related_works":["https://openalex.org/W4387497383","https://openalex.org/W3183948672","https://openalex.org/W3173606202","https://openalex.org/W3110381201","https://openalex.org/W2948807893","https://openalex.org/W2778153218","https://openalex.org/W2758277628","https://openalex.org/W1531601525","https://openalex.org/W1986582023","https://openalex.org/W2966829450"],"abstract_inverted_index":{"Imitation":[0],"learning":[1,27,69,98,105,131,156,176,210],"that":[2,101,150,200],"replicates":[3],"experts'":[4],"skills":[5,149],"via":[6],"their":[7],"demonstrations":[8,63],"has":[9],"shown":[10],"significant":[11],"success":[12],"in":[13,29,81,138,160],"various":[14],"decision-making":[15],"tasks.":[16],"However,":[17],"two":[18],"critical":[19],"challenges":[20],"still":[21],"hinder":[22],"the":[23,37,43,47,59,135,139,161,173,188,226,231],"deployment":[24],"of":[25,46,61,141],"imitation":[26,209,240],"techniques":[28],"real-world":[30],"application":[31],"scenarios.":[32],"First,":[33],"existing":[34],"methods":[35,211],"lack":[36],"intrinsic":[38],"interpretability":[39,186,216],"to":[40,58,106,116,178,229],"explicitly":[41],"explain":[42],"underlying":[44],"rationale":[45],"learned":[48,53],"skill":[49,97,130,175],"and":[50,87,112,123,144,157,183,196,239],"thus":[51],"making":[52],"policy":[54,71,104,155],"untrustworthy.":[55],"Second,":[56],"due":[57],"scarcity":[60],"expert":[62,142],"from":[64,109],"each":[65,117],"end":[66],"user":[67,119],"(client),":[68],"a":[70,94,167,218],"based":[72,174],"on":[73,193],"different":[74,110],"data":[75,108,124],"silos":[76],"is":[77,225],"necessary":[78],"but":[79,212],"challenging":[80],"privacy-sensitive":[82],"applications":[83],"such":[84],"as":[85,148],"finance":[86],"healthcare.":[88],"To":[89],"this":[90],"end,":[91],"we":[92,165],"present":[93],"privacy-preserved":[95],"interpretable":[96,129,236],"framework":[99,204,224],"(FedSkill)":[100],"enables":[102],"global":[103,180],"incorporate":[107],"sources":[111],"provides":[113],"explainable":[114],"interpretations":[115],"local":[118,185],"without":[120],"violating":[121],"privacy":[122],"sovereignty.":[125],"Specifically,":[126],"our":[127,201],"proposed":[128,202,222],"model":[132,177],"can":[133],"capture":[134],"varying":[136],"patterns":[137],"trajectories":[140],"demonstrations,":[143],"extract":[145],"prototypical":[146],"information":[147,181],"provide":[151],"implicit":[152],"guidance":[153],"for":[154],"explicit":[158],"explanations":[159],"reasoning":[162],"process.":[163],"Moreover,":[164],"design":[166],"novel":[168],"aggregation":[169],"mechanism":[170],"coupled":[171],"with":[172],"preserve":[179],"utilization":[182],"maintain":[184],"under":[187,217],"federated":[189,219,234],"framework.":[190],"Thoroughly":[191],"experiments":[192],"three":[194],"datasets":[195],"empirical":[197],"studies":[198],"demonstrate":[199],"FedSkill":[203,223],"not":[205],"only":[206],"outperforms":[207],"state-of-the-art":[208],"also":[213],"exhibits":[214],"good":[215],"setting.":[220],"Our":[221],"first":[227],"attempt":[228],"bridge":[230],"gaps":[232],"among":[233],"learning,":[235,238],"machine":[237],"learning.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
