{"id":"W4406518421","doi":"10.1016/j.knosys.2025.112965","title":"A survey on Deep Learning in Edge–Cloud Collaboration: Model partitioning, privacy preservation, and prospects","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick; Saint Mary's University","funders":"","keywords":"Cloud computing; Enhanced Data Rates for GSM Evolution; Computer science; Deep learning; Privacy protection; Internet privacy; Data science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.001787973,0.0002546781,0.0003499321,0.0004999578,0.0002982358,0.0006134895,0.005916592,0.0002108519,0.000001850307],"category_scores_gemma":[0.02162773,0.000261634,0.00002848236,0.002451204,0.00009681979,0.0007332602,0.007007187,0.000404334,0.00002772639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003238324,"about_ca_system_score_gemma":0.0005591672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001445827,"about_ca_topic_score_gemma":0.0009464201,"domain_scores_codex":[0.9973388,0.0005728625,0.0005578733,0.0008310387,0.0002954082,0.0004040422],"domain_scores_gemma":[0.9949332,0.0006742604,0.0002042211,0.00361471,0.0005028271,0.00007079072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001239099,0.00090291,0.4561584,0.001260935,0.00009238008,0.00002852166,0.001834353,0.1112609,0.0004681138,0.1097895,0.3115115,0.006568543],"study_design_scores_gemma":[0.0008100003,0.00008436814,0.01968292,0.0005106529,0.00000410238,6.168691e-7,0.00003169866,0.9641635,0.0006747885,0.01118784,0.002607211,0.0002423414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08203521,0.003761319,0.8966681,0.007601975,0.001598369,0.001758426,0.00003033587,0.001665823,0.004880417],"genre_scores_gemma":[0.9900339,0.0000196062,0.008609488,0.00007083965,0.00005199671,0.0003261407,0.00005929947,0.00001776502,0.0008110045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9079986,"threshold_uncertainty_score":0.9999836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03464741713182739,"score_gpt":0.2931637785600794,"score_spread":0.258516361428252,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}