{"id":"https://openalex.org/W4415936372","doi":"https://doi.org/10.48550/arxiv.2511.00269","title":"FedReplay: A Feature Replay Assisted Federated Transfer Learning Framework for Efficient and Privacy-Preserving Smart Agriculture","display_name":"FedReplay: A Feature Replay Assisted Federated Transfer Learning Framework for Efficient and Privacy-Preserving Smart Agriculture","publication_year":2025,"publication_date":"2025-10-31","ids":{"openalex":"https://openalex.org/W4415936372","doi":"https://doi.org/10.48550/arxiv.2511.00269"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.00269","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.00269","pdf_url":"https://arxiv.org/pdf/2511.00269","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.00269","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100408764","display_name":"Long Li","orcid":"https://orcid.org/0000-0002-1939-5941"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Long","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100454275","display_name":"Jiajia Li","orcid":"https://orcid.org/0000-0001-8158-3970"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jiajia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100319482","display_name":"Dong Chen","orcid":"https://orcid.org/0000-0003-2018-1120"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Dong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077083196","display_name":"Lina Pu","orcid":"https://orcid.org/0000-0002-5663-1501"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pu, Lina","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027637869","display_name":"Haibo Yao","orcid":"https://orcid.org/0000-0003-2967-0051"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Haibo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5006023502","display_name":"Yanbo Huang","orcid":"https://orcid.org/0000-0002-1409-8868"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yanbo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100408764"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.753600001335144,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.753600001335144,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.1526000052690506,"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.010300000198185444,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7574999928474426},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6477000117301941},{"id":"https://openalex.org/keywords/participatory-sensing","display_name":"Participatory sensing","score":0.489300012588501},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.47119998931884766},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4659999907016754},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4115999937057495},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.40119999647140503},{"id":"https://openalex.org/keywords/data-transmission","display_name":"Data transmission","score":0.39640000462532043}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8285999894142151},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7574999928474426},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6477000117301941},{"id":"https://openalex.org/C2779208394","wikidata":"https://www.wikidata.org/wiki/Q7140460","display_name":"Participatory sensing","level":2,"score":0.489300012588501},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4659999907016754},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44110000133514404},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4115999937057495},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C557945733","wikidata":"https://www.wikidata.org/wiki/Q389772","display_name":"Data transmission","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38339999318122864},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3668000102043152},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C137822555","wikidata":"https://www.wikidata.org/wiki/Q2587068","display_name":"Information sensitivity","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.27230000495910645},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.27219998836517334},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2703000009059906},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.2669000029563904},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25110000371932983}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.00269","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.00269","pdf_url":"https://arxiv.org/pdf/2511.00269","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.00269","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.00269","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.00269","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.00269","pdf_url":"https://arxiv.org/pdf/2511.00269","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"classification":[1,155],"plays":[2],"a":[3,56,62,72,101,119],"pivotal":[4],"role":[5],"in":[6],"smart":[7],"agriculture,":[8],"enabling":[9],"applications":[10],"such":[11],"as":[12],"crop":[13],"monitoring,":[14],"fruit":[15],"recognition,":[16],"and":[17,40,45,96,182,194],"pest":[18],"detection.":[19],"However,":[20],"conventional":[21],"centralized":[22],"training":[23,91],"often":[24],"requires":[25],"large-scale":[26,92],"data":[27,44,117],"collection,":[28],"which":[29,165],"raises":[30],"privacy":[31,143],"concerns,":[32],"while":[33,145],"standard":[34],"federated":[35,57,98,175,190],"learning":[36,58,176,191],"struggles":[37],"with":[38,71,189],"non-independent":[39],"identically":[41],"distributed":[42],"(non-IID)":[43],"incurs":[46],"high":[47],"communication":[48],"costs.":[49],"To":[50],"address":[51],"these":[52],"challenges,":[53],"we":[54],"propose":[55],"framework":[59,89],"that":[60,158],"integrates":[61],"frozen":[63],"Contrastive":[64],"Language-Image":[65],"Pre-training":[66],"(CLIP)":[67],"vision":[68],"transformer":[69,74],"(ViT)":[70],"lightweight":[73],"classifier.":[75],"By":[76],"leveraging":[77],"the":[78,84,88,159,180],"strong":[79],"feature":[80,125],"extraction":[81],"capability":[82],"of":[83,123,184],"pre-trained":[85],"CLIP":[86],"ViT,":[87],"avoids":[90],"models":[93],"from":[94,127],"scratch":[95],"restricts":[97],"updates":[99],"to":[100,110,139,173],"compact":[102],"classifier,":[103],"thereby":[104],"reducing":[105],"transmission":[106],"overhead":[107],"significantly.":[108],"Furthermore,":[109],"mitigate":[111],"performance":[112],"degradation":[113],"caused":[114],"by":[115],"non-IID":[116],"distribution,":[118],"small":[120],"subset":[121],"(1%)":[122],"CLIP-extracted":[124],"representations":[126],"all":[128],"classes":[129],"is":[130,166],"shared":[131,135],"across":[132,149],"clients.":[133],"These":[134],"features":[136,188],"are":[137],"non-reversible":[138],"raw":[140],"images,":[141],"ensuring":[142],"preservation":[144],"aligning":[146],"class":[147],"representation":[148],"participants.":[150],"Experimental":[151],"results":[152],"on":[153],"agricultural":[154,196],"tasks":[156],"show":[157],"proposed":[160],"method":[161],"achieve":[162],"86.6%":[163],"accuracy,":[164],"more":[167],"than":[168],"4":[169],"times":[170],"higher":[171],"compared":[172],"baseline":[174],"approaches.":[177],"This":[178],"demonstrates":[179],"effectiveness":[181],"efficiency":[183],"combining":[185],"vision-language":[186],"model":[187],"for":[192],"privacy-preserving":[193],"scalable":[195],"intelligence.":[197]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-05T00:00:00"}
