{"id":"W4402980138","doi":"10.1109/icme57554.2024.10688354","title":"FedRMS: Privacy-Preserving Federated Knowledge Graph Embedding Through Randomization","year":2024,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Research and Development; National Natural Science Foundation of China; National Research Foundation","keywords":"Computer science; Embedding; Graph; Theoretical computer science; Randomization; Knowledge graph; Information retrieval; Artificial intelligence; Randomized controlled trial; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003333922,0.001244568,0.001571688,0.00143721,0.001011266,0.001794508,0.00305826,0.001601354,0.00255656],"category_scores_gemma":[0.01466219,0.0005461476,0.001206624,0.00209616,0.001651829,0.007650166,0.004179566,0.002638874,0.001165373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214219,"about_ca_system_score_gemma":0.001874991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002360623,"about_ca_topic_score_gemma":0.003035856,"domain_scores_codex":[0.9956091,0.001617586,0.0002583344,0.001196887,0.00102781,0.0002902439],"domain_scores_gemma":[0.9892089,0.003882652,0.0007125434,0.005257628,0.0007017956,0.0002366085],"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":[0.0006522424,0.0005687107,0.003515837,0.0003283503,0.0002134789,0.000368222,0.0003532111,0.4205867,0.008156476,0.05076702,0.01584011,0.4986496],"study_design_scores_gemma":[0.00003561653,0.00007135767,0.000259106,0.0000188915,0.00002375684,0.0001348739,0.00005298494,0.9409087,0.004153075,0.05235237,0.001966968,0.00002231274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02764151,0.0006083664,0.9644203,0.0004656159,0.00007047321,0.0001286727,0.0006000975,0.004778576,0.001286349],"genre_scores_gemma":[0.7105605,0.0005690349,0.2808186,0.0005149386,0.0001210156,0.0002546974,0.003105366,0.0004113863,0.00364434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003333922,"threshold_uncertainty_score":0.01763171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03339685406955926,"score_gpt":0.3152906126988001,"score_spread":0.2818937586292408,"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."}}