{"id":"W4412081587","doi":"10.1109/tmc.2025.3586441","title":"Fed$n$nP: Federated Unlearning With Multiple Client Set Partitions","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions","keywords":"Computer science; Set (abstract data type); Theoretical computer science; Distributed computing; Computer network; Programming language","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002209287,0.0002692767,0.0002618302,0.0003173014,0.00119073,0.00030786,0.0006385282,0.00008888251,0.00002251834],"category_scores_gemma":[0.00001427197,0.0002478219,0.00008840853,0.00107651,0.00007846685,0.0005111439,0.00002475075,0.0005601979,0.00003676213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001259123,"about_ca_system_score_gemma":0.000105511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003307475,"about_ca_topic_score_gemma":0.00003263859,"domain_scores_codex":[0.9980167,0.0001598621,0.0003968037,0.0007102563,0.0002831219,0.0004332935],"domain_scores_gemma":[0.9983318,0.0005102007,0.000137429,0.0007369266,0.0001705643,0.0001130247],"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":[0.00005095927,0.0003647128,0.0001996225,0.00003336036,0.00006293751,0.00002097915,0.0003248847,0.7687801,0.002774829,0.000618184,0.001410704,0.2253587],"study_design_scores_gemma":[0.0008997374,0.0003991069,0.000163012,0.0004388395,0.00002205543,0.00002924562,0.0001544467,0.902824,0.08345217,0.0003004176,0.01085901,0.0004579529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01712901,0.00003430355,0.9795679,0.0001667669,0.0004406871,0.000526819,0.00001880683,0.001612034,0.0005036789],"genre_scores_gemma":[0.8671772,0.00001228215,0.1319951,0.0003705823,0.00001718616,0.000143342,0.000008562484,0.00001882369,0.000256848],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8500482,"threshold_uncertainty_score":0.9999974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01478419958080761,"score_gpt":0.2867042894395307,"score_spread":0.2719200898587231,"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."}}