{"id":"W4408325865","doi":"10.1109/globecom52923.2024.10901830","title":"Generalization vs Personalization: A Trade-off for better Data Heterogeneity impact Mitigation in FL","year":2024,"lang":"en","type":"article","venue":"","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Personalization; Generalization; Computer science; Mathematics; World Wide Web","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":[0.00391989,0.001241643,0.001181331,0.0008225733,0.0007537295,0.001322566,0.001772635,0.001408209,0.001216783],"category_scores_gemma":[0.01125687,0.0003368004,0.0007676668,0.001000893,0.0007982559,0.005027352,0.002177064,0.00169625,0.000641921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007753504,"about_ca_system_score_gemma":0.0009248477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002943921,"about_ca_topic_score_gemma":0.003591798,"domain_scores_codex":[0.9975792,0.0006411722,0.0001947306,0.0008293702,0.0004758108,0.0002798834],"domain_scores_gemma":[0.9945582,0.001994785,0.000366687,0.002155673,0.0007471701,0.0001775232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009104771,0.0007734225,0.01298687,0.0002078923,0.0002582383,0.0003158542,0.0006235962,0.2361242,0.02313324,0.003881649,0.00685984,0.7139248],"study_design_scores_gemma":[0.00005603606,0.0004063213,0.004470911,0.00004005709,0.0000758473,0.0003221812,0.0002811326,0.9651447,0.01702853,0.007942853,0.004186501,0.00004482444],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1875519,0.001348069,0.7991665,0.001505428,0.000143768,0.0002267508,0.000339325,0.007123425,0.002594773],"genre_scores_gemma":[0.8629709,0.0002719305,0.1327945,0.0007578309,0.000128194,0.0001535164,0.0006500922,0.0002163219,0.002056761],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00391989,"threshold_uncertainty_score":0.02073056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04194026601879277,"score_gpt":0.3242700396661313,"score_spread":0.2823297736473385,"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."}}