{"as_of":"2026-08-08T00:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8ab7177b5d7183ecb43bea10d1dc7e9a55843b6c9ddc378dd8309751b124cc4c","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:49:34.918562Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.02021/citation-record","integrity":"/paper/2507.02021/integrity","json":"/paper/2507.02021/citation-record.json","paper":"/paper/2507.02021"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:38.855402Z","title":"On softwarization of intelligence in 6G networks for ultra-fast optimal policy selection: Challenges and opportunities,","venue":null,"work_id":"79e64fbc-48f7-4cff-aa61-5e462407b239","year":2023},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:32.803298Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:41ab6881fe9d3405dd3b279f4413009f1b9fe91bc37ced6e43321fb2b72e6f0d","observation_id":"a54f9111-dad6-4eab-9f7d-36fc9b2ec231","resolution":{"observed_at":"2026-08-06T20:49:38.949165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:32.909944Z","title":"Privacy-preserving data-driven learning mod- els for emerging communication networks: A comprehensive sur- vey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:32.909944Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:0065dd867993ae4872717e0d604d94060c22747721224b396f13c87c790c07e5","observation_id":"5aa3e935-3508-41d0-83d2-1aa23fc9be00","resolution":{"observed_at":"2026-08-06T20:49:32.909944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:38.735856Z","title":"A survey on federated learning,","venue":null,"work_id":"4ec1ed26-7cce-47fa-924b-ad32a822ceb7","year":2021},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:32.991795Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:985390410c13136d512e636363e5cbbeb94432929ad6d1fc1a4965ff140d4b79","observation_id":"d8cdf758-8b39-4808-94f9-ecccfe554e32","resolution":{"observed_at":"2026-08-06T20:49:38.807652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:38.544418Z","title":"HCP: heterogeneous computing platform for federated learning based collaborative content caching towards 6g networks,","venue":null,"work_id":"03e8edb5-cc9c-4e76-89e5-4958ac7a46bb","year":2022},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:33.061699Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:846c4c0b5d4fba20fd317ad23c79aba35864444ffa9c3b3a83660e8817c91d56","observation_id":"3a0f2da2-72e2-4af0-9a1d-46892efc16d2","resolution":{"observed_at":"2026-08-06T20:49:38.633338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:38.363232Z","title":"Privacy-preserving federated- learning-based net-energy forecasting,","venue":null,"work_id":"559a0d55-a3e9-4c9f-b092-290e491c1c4c","year":2022},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:33.151924Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:ed35d2ea06d6bce7e1ec41d2d49a21b7b73afc8bf901eb7619a6b52b7881657b","observation_id":"7e37ed2e-db00-4348-a59a-1b7af5f25c18","resolution":{"observed_at":"2026-08-06T20:49:38.477327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:38.196844Z","title":"Privacy-preserving and efficient decentralized federated learning-based energy theft detector,","venue":null,"work_id":"62e0d656-ba8a-4c51-a4da-0933ada343cc","year":2022},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:33.215163Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:2b9d18564b941f0b98cac04fbdd12a583cabdd8358a0310c3d5bfa0811a0bde2","observation_id":"50adc746-30d6-4d64-b74d-44c42664fca9","resolution":{"observed_at":"2026-08-06T20:49:38.280003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:37.999010Z","title":"On COVID-19 prediction using asynchronous federated learning-based agile radiograph screening booths,","venue":null,"work_id":"21a528e4-ecb9-45dd-80f8-35d2a3d444ad","year":2021},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:33.277751Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:99265078764c10a2fd32d9d8d937b6f9d3ee778610439890ad368651b42d0660","observation_id":"60bf914a-b2c8-4bb3-82d9-91d17d3bb796","resolution":{"observed_at":"2026-08-06T20:49:38.100140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:37.810120Z","title":"Asynchronous federated learning-based ECG analysis for arrhythmia detection,","venue":null,"work_id":"c29777cf-9419-4d5b-a064-570fd2916eba","year":2021},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:33.378977Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:7fb394c4558d3593b56d388705f16b9d378db21759f2c80961a52907235bda47","observation_id":"84382b43-8665-4793-a50b-f13a7d010418","resolution":{"observed_at":"2026-08-06T20:49:37.902499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:37.621373Z","title":"Toward asynchronously weight updating federated learning for AI-on-edge IoT systems,","venue":null,"work_id":"65e9bdb0-a042-4d57-a5ad-80fcf92ee60d","year":2022},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:33.645290Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:8d612494a48bd1d76c450eb03667310427343b9f21bc1d385482a58175f4b80d","observation_id":"d082e4e7-be88-42bc-8ec2-f8746d11b051","resolution":{"observed_at":"2026-08-06T20:49:37.690165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:37.414425Z","title":"A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept,","venue":null,"work_id":"a2099e68-c65c-4383-b226-afe7b88c478d","year":2022},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:33.894409Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:3b49e08d59cea729443ae013b7b20a2a3653019814b10617db9d7150de34b707","observation_id":"66c33ed3-a2c9-4d8b-aa57-5eb0a3742346","resolution":{"observed_at":"2026-08-06T20:49:37.535405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:37.227098Z","title":"Adaboost-based security level classification of mobile intelligent terminals,","venue":null,"work_id":"0d2a7207-ac02-4c81-ac1e-f7ed15f8d337","year":2019},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:33.980420Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:c4bbe6460ca26a60a5019ac837f29e69f53c0190285754899ff58bf4b4215e86","observation_id":"6bf20ccf-e3ba-48c5-9203-c70b6928c50f","resolution":{"observed_at":"2026-08-06T20:49:37.337229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:37.091369Z","title":"Joint provisioning of QoS and se- curity in IoD networks: Classical optimization meets AI,","venue":null,"work_id":"f9c96f78-904b-4018-b9ca-a45d1348dcb3","year":2021},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.063841Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:ced132d867dd974ee0e452a6881270dab7fa3a23431e80681a9fda961c2dc7a7","observation_id":"476e3df9-bf26-4c76-8d99-8d07216c0757","resolution":{"observed_at":"2026-08-06T20:49:37.158090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:36.944610Z","title":"CICIoT2023: A real-time dataset and benchmark for large- scale attacks in IoT environment,","venue":null,"work_id":"9ba20a37-1d4e-407f-a541-1d1fdcc3115c","year":2023},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.144594Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:32a200a1d98cf2dec6ed9c262b1cf895e7231bdb37c093488d9e798f8c696639","observation_id":"0baeb346-48cd-46a6-aec8-451846776800","resolution":{"observed_at":"2026-08-06T20:49:37.015226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:36.736626Z","title":"Data resampling for federated learning with non-IID labels,","venue":null,"work_id":"f42f80c6-c14f-4f0e-9e2d-514d096e4383","year":2021},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.209633Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:86896b94fef8bc18e266a5639115e3ed7215ed7d67abbbb6983dd946afb8a47c","observation_id":"b763b505-9664-46a9-a33f-66440df3e18f","resolution":{"observed_at":"2026-08-06T20:49:36.821475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:36.538884Z","title":"AdaBoost-CNN: an adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning,","venue":null,"work_id":"df882345-0122-4344-827e-bbfc02355e85","year":2020},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.289797Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:fe5f12a36224bdb16c716be38e95b3038dd43779f3f7e4ca18c0cb689c29188b","observation_id":"9f7daa3e-d8a9-48b0-9f54-d48bbe906065","resolution":{"observed_at":"2026-08-06T20:49:36.637285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:36.343642Z","title":"LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computa- tional complexity on IID and non-IID intensive care data,","venue":null,"work_id":"90aac2e5-03b4-46cf-b144-fc61bde368a0","year":2020},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.363555Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:b2277b3a1f23b8ac667d081d8910e0d80b8f2c6e3a9ec46bb5be3a291a19a5d1","observation_id":"f2603f46-7343-40b3-9751-ead334709d1e","resolution":{"observed_at":"2026-08-06T20:49:36.440808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:36.206774Z","title":"Differential privacy for deep and federated learning: A survey,","venue":null,"work_id":"2360c3a5-fbc9-4675-8909-6e2e6fce83d1","year":2022},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.435860Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:3ef61b7820b8b5f93df8cb7f6c998dd810158652be47dc532d7555627ee431f2","observation_id":"655d47dd-4912-45d9-86a7-6a480555dfbd","resolution":{"observed_at":"2026-08-06T20:49:36.269694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:36.001680Z","title":"Federated learning with differential privacy: Algorithms and performance analysis,","venue":null,"work_id":"4c2427d1-5c30-4d69-a457-3896c7dfb7a6","year":2020},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.526727Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:2cb2304f72733cd379cf5355158447c192e0a089d1a336575abef18a5f93fd78","observation_id":"d148bc92-673b-4155-b4fc-94a27332a3f3","resolution":{"observed_at":"2026-08-06T20:49:36.079212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:35.837854Z","title":"Evaluating differentially private machine learning in practice,","venue":null,"work_id":"d0849338-4ca9-4265-a65c-c1b6c515e2dd","year":2019},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.598255Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:b2232a8e28a7701d313f1e611c3abaea9aa7751293056f89c52e4489764de964","observation_id":"0075fad0-8567-4f16-9ec3-588a8970ed20","resolution":{"observed_at":"2026-08-06T20:49:35.927064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:35.682468Z","title":"Communication and computation efficiency in federated learning: A survey,","venue":null,"work_id":"7c005846-eafe-440d-812c-fc2884aa9be3","year":2023},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.668476Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:10b964527405d5185f08f5a4f3b0b5815ef85beab78ad7db42ba808050660e4a","observation_id":"175f1fa6-ca95-433f-a0bc-63f54fe6ab7c","resolution":{"observed_at":"2026-08-06T20:49:35.774349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:35.473027Z","title":"Communication-efficient federated learning via quantized compressed sensing,","venue":null,"work_id":"76e5c7f9-e3e6-4050-987e-73551929fc31","year":2022},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.757491Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:357a2558afcf83f1bf77390fabb01f923ba4a19eabafd93f515a8c68edef6830","observation_id":"f50b097f-5faa-4ec2-9cc6-334a7e34035b","resolution":{"observed_at":"2026-08-06T20:49:35.564305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:35.282536Z","title":"A robust federated learning approach for combating attacks against IoT systems under non-IID challenges,","venue":null,"work_id":"808dc1c1-8cca-4f4a-8f8e-0f6822ee324f","year":2024},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.847014Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:c41bc15d0fbe0f954d88ad3e5b681608e86f74b1b3c5d75c24cb0b163ced778b","observation_id":"128c38ac-cd35-4f02-a7b8-ca4d1df07f83","resolution":{"observed_at":"2026-08-06T20:49:35.372153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:49:35.078023Z","title":"Combating IoT attacks in AI-driven networks via robust and resource-efficient federated learning,","venue":null,"work_id":"f9e94b68-ebda-4330-a705-5d2963905b82","year":2024},"citing_paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:34.918562Z"},"links":{"citing_paper":"/paper/2507.02021"},"observation_digest":"sha256:063c76c99e26f4c9e8dc536fd6eac4e96e1238e3d3614755143a09e31dae8708","observation_id":"066b3a43-7ae5-42cc-8baf-43ab7b90c61b","resolution":{"observed_at":"2026-08-06T20:49:35.165884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.02021","last_updated":"2025-07-02T14:41:25Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-06T20:40:55.196379Z","submitted_at":"2025-07-02T14:41:25Z","title":"REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":22},"total_outbound_references":23},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2507.02021."}