{"id":"W4320039274","doi":"10.1007/s10664-022-10262-y","title":"Towards understanding quality challenges of the federated learning for neural networks: a first look from the lens of robustness","year":2023,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; University of Alberta; University of Calgary","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Computer science; Machine learning; Artificial intelligence; Artificial neural network; Deep learning; Data mining; Deep neural networks; Process (computing)","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":[0.03041368,0.001047766,0.002453238,0.002623996,0.001299862,0.01079419,0.00401982,0.004838549,0.004308551],"category_scores_gemma":[0.1551442,0.001099156,0.001681537,0.002218103,0.01075219,0.02982787,0.007369714,0.008074089,0.0004555666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003935039,"about_ca_system_score_gemma":0.003280433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002784051,"about_ca_topic_score_gemma":0.001361845,"domain_scores_codex":[0.9825847,0.008643107,0.001008183,0.002129945,0.004817728,0.000816487],"domain_scores_gemma":[0.8661731,0.09274051,0.009000159,0.02194425,0.008753353,0.001388528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009916197,0.00007110637,0.002571973,0.0001974111,0.0001167705,0.0000705483,0.0003245991,0.04743399,0.0005359045,0.9230433,0.001425691,0.02410963],"study_design_scores_gemma":[0.000009247159,0.00002505377,0.000397307,0.00007131888,0.00001571174,0.00003434907,0.00010966,0.08615491,0.000347977,0.9115112,0.001307909,0.00001540111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03902735,0.004117801,0.9288759,0.02137571,0.0001648061,0.00006233822,0.0002629651,0.0001939591,0.005919036],"genre_scores_gemma":[0.8728012,0.004618602,0.1168064,0.001720973,0.0009980714,0.0001419325,0.0002988678,0.0002724637,0.002341412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03041368,"threshold_uncertainty_score":0.1608448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1321526516824001,"score_gpt":0.3088522007461654,"score_spread":0.1766995490637653,"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."}}