{"id":"W4312985618","doi":"10.1109/jstsp.2022.3224591","title":"Privacy-Preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Guangdong Provincial Pearl River Talents Program; National Research Foundation of Korea; National Natural Science Foundation of China; Ministry of Science and ICT, South Korea; Singapore University of Technology and Design; Ministry of Education - Singapore","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; The Internet; Computer network; Resource allocation; Edge computing; Internet privacy; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.002023723,0.0001990752,0.0003529728,0.0008172937,0.0002606028,0.0003309942,0.01090065,0.0001149884,0.00001370004],"category_scores_gemma":[0.007431374,0.0002108845,0.00005976162,0.001998631,0.00004295558,0.0009458447,0.008936832,0.001695561,5.74153e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006761563,"about_ca_system_score_gemma":0.0004984258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002746262,"about_ca_topic_score_gemma":0.00001799268,"domain_scores_codex":[0.9972036,0.0003401804,0.001014547,0.0004047171,0.0005630268,0.0004738944],"domain_scores_gemma":[0.99767,0.0003325049,0.0007832529,0.0007256276,0.0004279602,0.00006066218],"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":[0.0009166554,0.001415159,0.0199692,0.0008330081,0.0001569353,0.0004987057,0.01142436,0.1254548,0.07471769,0.001230033,0.05272102,0.7106625],"study_design_scores_gemma":[0.000687074,0.0004467838,0.0005147482,0.0003439354,0.00000696328,0.0001251988,0.0004290219,0.946595,0.01843329,0.02677748,0.005390218,0.0002503497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2979626,0.000851877,0.6925735,0.007825368,0.0002856573,0.0002678012,0.000001209213,0.0001478901,0.00008411395],"genre_scores_gemma":[0.9512237,0.00002475589,0.04837203,0.0001212491,0.0001300655,0.00002806502,0.00000616724,0.00002334198,0.00007064716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8211402,"threshold_uncertainty_score":0.9990787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03912346999838315,"score_gpt":0.2894215025369622,"score_spread":0.2502980325385791,"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."}}