{"id":"W4412403981","doi":"10.1109/jiot.2025.3589179","title":"Advancing Privacy and Fairness in Healthcare Using Federated Edge Learning and Blockchain","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blockchain; Computer science; Health care; Information privacy; Internet privacy; Computer security; Enhanced Data Rates for GSM Evolution; Edge computing; Computer network; Internet of Things; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001132773,0.0001583956,0.0002969963,0.0004906871,0.000144921,0.0003438653,0.00415233,0.0001252099,0.000001610135],"category_scores_gemma":[0.005799639,0.0001543769,0.00002998984,0.0003967346,0.0000986857,0.0006910587,0.01360184,0.001020752,2.698866e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000160068,"about_ca_system_score_gemma":0.0001146971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00041272,"about_ca_topic_score_gemma":0.00001994498,"domain_scores_codex":[0.9984671,0.0001552154,0.0004911939,0.0003438144,0.0001997175,0.0003429725],"domain_scores_gemma":[0.9986675,0.0001867833,0.0003127763,0.0006320856,0.0001283479,0.00007245687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002520412,0.0002854351,0.3849949,0.001521542,0.0003402177,0.0008838879,0.010888,0.00100323,0.09280597,0.006426737,0.01531253,0.4852855],"study_design_scores_gemma":[0.0007603116,0.0001262012,0.001447636,0.002079328,0.00000826587,0.0006124862,0.0002859267,0.8952612,0.02147321,0.07741284,0.0002966869,0.000235901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6847746,0.001045973,0.3035224,0.01004352,0.0004093861,0.00007293538,3.841498e-7,0.00008801345,0.0000427358],"genre_scores_gemma":[0.8871601,0.0001280964,0.112459,0.0001907712,0.00001676818,0.000001133066,2.035125e-7,0.000007984905,0.0000359349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.894258,"threshold_uncertainty_score":0.9943759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897484061754232,"score_gpt":0.3042390651897909,"score_spread":0.2852642245722485,"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."}}