{"id":"W4416650133","doi":"10.1109/jsac.2025.3636844","title":"Toward Double-Layer Data Privacy in Communication-Efficient Hierarchical Federated Learning: A Client Sampling Approach","year":2025,"lang":"","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Carleton University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China; Science and Technology Foundation of Shenzhen City","keywords":"Differential privacy; Server; Overhead (engineering); Sampling (signal processing); Cloud computing; Information privacy; Noise (video)","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":[0.009014267,0.001275719,0.002016532,0.0006869832,0.001076036,0.00249357,0.00434008,0.002403398,0.001651662],"category_scores_gemma":[0.02608628,0.0007601159,0.001222225,0.001337618,0.002551722,0.00478556,0.005926193,0.004205176,0.0005049909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002113306,"about_ca_system_score_gemma":0.003307309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001827365,"about_ca_topic_score_gemma":0.001716683,"domain_scores_codex":[0.9911801,0.00413309,0.0003527223,0.001508701,0.001961417,0.000863974],"domain_scores_gemma":[0.9815408,0.009907581,0.001169449,0.005304476,0.001363579,0.00071404],"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":[0.0008040991,0.0003294862,0.003332231,0.0001916405,0.0001615927,0.0003564976,0.0004623506,0.7743172,0.005340386,0.1213896,0.003230827,0.09008414],"study_design_scores_gemma":[0.00002126895,0.00005037201,0.00009192249,0.000008765656,0.000008450504,0.00005662159,0.00002236502,0.967862,0.001614094,0.02990306,0.0003522177,0.000008851576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01225,0.0001088751,0.9859789,0.0004068041,0.00001274494,0.00005934143,0.00006254579,0.000419273,0.0007015945],"genre_scores_gemma":[0.8161286,0.0002032656,0.1797256,0.0006603933,0.00008246855,0.0002917416,0.0002231196,0.0001218803,0.002562836],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009014267,"threshold_uncertainty_score":0.04767257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1904325425658481,"score_gpt":0.3814537212523984,"score_spread":0.1910211786865503,"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."}}