{"id":"W4407354563","doi":"10.1109/tmc.2025.3541191","title":"Optimizing Federated Semantic Learning in Distributed AIGC-Enabled Human Digital Twins: A Multi-Criteria and Multi-Shard User Selection Framework","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education - Singapore","keywords":"Computer science; Selection (genetic algorithm); Distributed learning; Distributed computing; Human–computer interaction; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001771944,0.0007876116,0.001247728,0.0004699339,0.0005639935,0.00113687,0.001551436,0.001214275,0.001237577],"category_scores_gemma":[0.003106833,0.000420332,0.000478241,0.0007366904,0.001212933,0.001654779,0.001827989,0.0009713082,0.0002504357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009719164,"about_ca_system_score_gemma":0.001432723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002805075,"about_ca_topic_score_gemma":0.002569123,"domain_scores_codex":[0.9988886,0.0003947612,0.00004584917,0.0002506998,0.0002329634,0.0001872009],"domain_scores_gemma":[0.9984822,0.0007853456,0.0001577766,0.0001691114,0.0002757681,0.0001297508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001835295,0.0001170976,0.0008826534,0.00005020974,0.00004627241,0.0001783143,0.0001046977,0.9280269,0.003468939,0.01325529,0.000833058,0.05285306],"study_design_scores_gemma":[0.000004978397,0.00002577715,0.00004796583,0.00000140454,0.000004327526,0.00001689064,0.000009165034,0.9968549,0.0005261305,0.002388109,0.0001166279,0.000003661124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02993069,0.0001300104,0.968352,0.0001291719,0.00002119247,0.00003429481,0.0000267938,0.0002166649,0.001159162],"genre_scores_gemma":[0.9140851,0.0001032297,0.0827946,0.0001063603,0.00002996002,0.00007605382,0.00006470907,0.00004478139,0.002695255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002805075,"threshold_uncertainty_score":0.009371042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02530336937577078,"score_gpt":0.3048579214552302,"score_spread":0.2795545520794595,"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."}}