{"id":"W4406094227","doi":"10.1109/ton.2024.3520530","title":"Toward Optimized Federated Learning With Compressed Communications by Rate Adaption","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Networking","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China; National Research Foundation","keywords":"Computer science; Multimedia; Human–computer interaction","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":[0.003453571,0.001463274,0.001545673,0.0007447326,0.0006730597,0.001374724,0.002801622,0.001900512,0.00144487],"category_scores_gemma":[0.01489921,0.0006083849,0.0005426293,0.0009478606,0.001769555,0.003536943,0.002996132,0.002884342,0.0006821095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216937,"about_ca_system_score_gemma":0.001947017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003270098,"about_ca_topic_score_gemma":0.002926863,"domain_scores_codex":[0.9983824,0.0005565428,0.00007675879,0.0003443125,0.0004378837,0.0002020405],"domain_scores_gemma":[0.9946824,0.002690619,0.0004200872,0.001353691,0.0006688327,0.0001843705],"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":[0.0002499664,0.0001489489,0.000971685,0.00004989311,0.00004167728,0.00009466649,0.0001207824,0.8976224,0.00210815,0.01276945,0.002795184,0.0830272],"study_design_scores_gemma":[0.00001010616,0.00001640069,0.00003254696,0.000003466667,0.000002944017,0.00001268462,0.000006288437,0.9933382,0.0005746876,0.005862014,0.0001374624,0.000003135536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04027722,0.0003212379,0.9542755,0.0005947726,0.000052737,0.00006694799,0.00008675014,0.002060049,0.002264594],"genre_scores_gemma":[0.8175476,0.0002069089,0.1783082,0.0005309947,0.0001072564,0.0002053378,0.0002952282,0.0002281148,0.002570375],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003453571,"threshold_uncertainty_score":0.01826441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0420380402731293,"score_gpt":0.2724648518237101,"score_spread":0.2304268115505808,"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."}}