{"as_of":"2026-08-08T22:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6e60bdf303a5c4216cf72331a73f36bdb914dc13fb0c87741a74dab088b199d7","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:22:50.322024Z","state":"measured"},{"denominator":75,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":75,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2505.19263/citation-record","integrity":"/paper/2505.19263/integrity","json":"/paper/2505.19263/citation-record.json","paper":"/paper/2505.19263"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:22:51.629705Z","title":"A communication-efficient federated learning scheme for IoT-based traffic forecasting,","venue":null,"work_id":"4db764dd-0dcc-4d74-92dd-17b36eb03ee7","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:47.631323Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:f36f2853487d1a25f87ed58e477ed398c4e703899b2fcbb99cf54390b2cc911d","observation_id":"b7739c2b-e2f5-4a97-962d-f090c86caaa0","resolution":{"observed_at":"2026-08-07T14:22:51.635309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.614831Z","title":"HCP: Heterogeneous computing platform for federated learning based collaborative content caching towards 6G networks,","venue":null,"work_id":"f01e2ecf-1e72-40f7-a2ee-72635261a468","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:47.813586Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:ac31811b78d3f8ca18c93dab984551e86bba3455ec0ac67f87d2d04b11e5002e","observation_id":"d544de63-5b24-4cc5-9959-152d55049c82","resolution":{"observed_at":"2026-08-07T14:22:51.619851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.600113Z","title":"Unsupervised deep learning for iot time series,","venue":null,"work_id":"fbf0c870-101e-4ffa-bd3b-eafe944b63ac","year":2023},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:48.031148Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:9c070b04c799d2820019cb80e861861042ce9dc74c1b80b0e5b9fa68bc1871e6","observation_id":"07a5c663-27be-447c-b975-67d25f15d70d","resolution":{"observed_at":"2026-08-07T14:22:51.605048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.585097Z","title":"AI-empowered network root cause analysis for 6G,","venue":null,"work_id":"a5b1f4e5-d1c1-4e94-93cf-15a34444ec07","year":2023},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:48.230608Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:825ba83bbe63273a238b57c0f82150dd7c1536e469322935137ec9eed3c18f65","observation_id":"152d4f57-a8e0-4b96-8082-669fed03773e","resolution":{"observed_at":"2026-08-07T14:22:51.589883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.570255Z","title":"TimeAutoML: Autonomous representation learning for multivariate irregularly sampled time series,","venue":null,"work_id":"6d6631fc-a608-4d3c-b31a-c03a51e63c38","year":2010},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:48.288143Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:46c678a733a2da5331a3ec07a358d1fc6f40e85d72ccc1cc443e56a7f80ecafe","observation_id":"1cef0638-41d3-419a-b2e8-2f556c17f11e","resolution":{"observed_at":"2026-08-07T14:22:51.575352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.554828Z","title":"Anomaly detection in event-triggered traffic time series via similarity learning,","venue":null,"work_id":"79879b23-81c7-4def-8db2-d433f995f911","year":2024},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:48.409244Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:542747703167fa746c85fc79b842ec3f5c3784a28fa024a5cd16630f5a22878d","observation_id":"74b9af91-1cea-46bd-8173-59da1eb907d7","resolution":{"observed_at":"2026-08-07T14:22:51.560099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.539835Z","title":"Active learning for wireless iot intrusion detection,","venue":null,"work_id":"675441db-c21b-4ee6-a314-b23878f723d1","year":2018},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:48.509087Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:c4ff97071f83b112df1dc6042adcfc7ec8b1c7e84fb2b36b5c5b2b04c035393c","observation_id":"8bd27489-9f91-448a-9395-537fbf610537","resolution":{"observed_at":"2026-08-07T14:22:51.544804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.525646Z","title":"Pc2a: Predicting collective contextual anomalies via lstm with deep generative model,","venue":null,"work_id":"cf327972-3018-486a-be22-265491ec0b1e","year":2019},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:48.619280Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:a6a607b27f0a5af0f110dfad7bc142fcb1e12e8ef66ea33f4ec5c13f8f27744b","observation_id":"c62e9b4c-bc4e-48b2-ab84-162054580f18","resolution":{"observed_at":"2026-08-07T14:22:51.530253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.511657Z","title":"LNTP: An end-to-end online prediction model for network traffic,","venue":null,"work_id":"d979f404-cab9-4cfa-936b-b2e6585b9b16","year":2021},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:48.769705Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:46fe5435433a66512a516a370d6663fa07e1cec3332cceeb9d5ad8f380f77e03","observation_id":"397b3269-6a46-4672-a4e8-98f8d2da4b9b","resolution":{"observed_at":"2026-08-07T14:22:51.516317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.496987Z","title":"Toward QoS prediction based on temporal transformers for IoT applications,","venue":null,"work_id":"815397ca-75c2-4174-8b03-852dea2545f9","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:48.887439Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:72704508126af8aca168fa55dee28ce9d18232fdefedabd2165c016141a0d91c","observation_id":"b2995f0c-90ca-47d2-975c-179287ac14d7","resolution":{"observed_at":"2026-08-07T14:22:51.501650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.481621Z","title":"Cellular traffic prediction via deep state space models with attention mechanism,","venue":null,"work_id":"3a5187cb-6ec4-4c8e-a9b0-49b0c92a83fb","year":2023},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:48.969984Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:529cf6b782d4da1161f58d484cfaf17367c432c39ca11bbceb8939d3692ab0f1","observation_id":"f9f1e97f-b8b3-4382-a9cd-ed0f6813b1c2","resolution":{"observed_at":"2026-08-07T14:22:51.487236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.465720Z","title":"Optimiza- tion design for federated learning in heterogeneous 6G networks,","venue":null,"work_id":"18fb63d0-a33e-4b48-8b68-61eaf603668c","year":2023},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:49.114808Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:53e8505dbe20923e9582db5c9d7f69e3b9a659a60e7a7d9641c5a13d99c28774","observation_id":"6185228c-4e97-427c-bf1d-29f06114332f","resolution":{"observed_at":"2026-08-07T14:22:51.470281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.451217Z","title":"Inverting gradients - how easy is it to break privacy in federated learning?","venue":null,"work_id":"caf09b26-ef42-491d-84ce-30bad05f5d70","year":2020},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:49.284428Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:5eb6ddc718b29661ab9aedfae44d0bd909ffc36dffe5d2e5cd63f1a8943e3cf8","observation_id":"8451a66d-9976-4984-94b2-59e1d628ac78","resolution":{"observed_at":"2026-08-07T14:22:51.455854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.436014Z","title":"Differential privacy in deep learning: An overview,","venue":null,"work_id":"968f5a7b-30b1-44c3-91e0-46fdd825620d","year":2019},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:49.406057Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:268e18c197785ea15e1e9e069855d22bd486bc02fab645e77e484776d089445f","observation_id":"27ba58d4-94c5-4a25-9983-6e0d137ee3c3","resolution":{"observed_at":"2026-08-07T14:22:51.441206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.418502Z","title":"A robust game- theoretical federated learning framework with joint differential privacy,","venue":null,"work_id":"571f99ec-cb81-46e2-8c49-b952af75f74e","year":2023},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:49.525863Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:3cdee4c9c51d245fe61e765f439779026bb7a2b66e21252cb1daa7bd34961267","observation_id":"8c1a4117-749d-44d4-ae8a-e716fb596f9c","resolution":{"observed_at":"2026-08-07T14:22:51.424586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.404115Z","title":"Distributionally robust federated learning for differentially private data,","venue":null,"work_id":"d6b180da-34f6-4bd6-b9b9-1f128645156d","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:49.602706Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:d5fe0aeab2c236a686f0c846c9b1afc5859c7d33d2ab70a9dfe8251e293fbd41","observation_id":"c68a6a84-92b6-41bd-9f8a-9133b5725d59","resolution":{"observed_at":"2026-08-07T14:22:51.408935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.388780Z","title":"Distributed robust optimization (DRO) part I: Framework and example,","venue":null,"work_id":"62533dc9-8fa7-4765-8f0a-16db51ac7b8b","year":2012},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:49.680574Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:09d7885d7735345d6231ce63604aa71c8eec8913fd2ae3cf14f2a1d8da3b999e","observation_id":"e8c97193-5997-4be8-a1cf-b3f551ee7c5e","resolution":{"observed_at":"2026-08-07T14:22:51.394026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.373291Z","title":"Distributed robust optimization for communication networks,","venue":null,"work_id":"3bd49b68-05f7-4467-b18c-f848439f4c75","year":2008},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:49.775923Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:27b511e97586ef032b9620bc7108205d56603a9526d792d489ee9a7fc6e96e15","observation_id":"b284a7d5-6ada-4d31-bcd6-6de2915aa226","resolution":{"observed_at":"2026-08-07T14:22:51.378521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.357739Z","title":"Machine learning with adversaries: Byzantine tolerant gradient descent,","venue":null,"work_id":"2da9dead-e283-496e-9f52-4bc2909c9661","year":2017},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:49.897570Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:f3deaf75c4e52e52f8c9c09cacfb093a9afc5562f1af10e5023b1a1faee0075b","observation_id":"6e95c836-2c88-4b05-8fa7-733b474a7857","resolution":{"observed_at":"2026-08-07T14:22:51.362573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.332830Z","title":"Byzantine-robust distributed learning: Towards optimal statistical rates,","venue":null,"work_id":"d30e285b-f39a-4092-9dd5-e22b3c5834dc","year":2018},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.030400Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:7df23fcca5780fe3251d51417ca135b42ba6b3dde5380e0697560e9913e08cbd","observation_id":"d60921c0-626c-4513-814e-bbfcbcf3a2f5","resolution":{"observed_at":"2026-08-07T14:22:51.337743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.318554Z","title":"Certifiably Byzantine-robust federated conformal prediction,","venue":null,"work_id":"4755accc-d1c2-43da-b4f6-41c75d93350e","year":2024},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.042981Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:77c64bf8666cd9efbef9e13c9f11f06565eef241488327e070ffcd158b1f9504","observation_id":"e135a387-b254-4888-96cf-6d99cbc78135","resolution":{"observed_at":"2026-08-07T14:22:51.322985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.303343Z","title":"RSA: byzantine- robust stochastic aggregation methods for distributed learning from heterogeneous datasets,","venue":null,"work_id":"ed958f95-a510-47d7-86af-617bfc3f9bcc","year":2019},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.064643Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:6b83a9986f5699f60eeea303906f9480b5d8e6bf303f51fdab56d7055cf63870","observation_id":"72fff356-148c-463a-8f1c-50d6608b1fdc","resolution":{"observed_at":"2026-08-07T14:22:51.308592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.288506Z","title":"Bridging differential privacy and Byzantine- robustness via model aggregation,","venue":null,"work_id":"489d436d-8db0-4362-b02a-d825717ed5c6","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.070618Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:d5439a6609cbecfeabf6d4746980cac420d88e7f11cc95c79cd8b6c21721078a","observation_id":"ef3fd120-5e64-4bfe-b877-51c303db8e47","resolution":{"observed_at":"2026-08-07T14:22:51.293594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.271488Z","title":"Differentially private byzantine-robust federated learning,","venue":null,"work_id":"e11c5fc0-9fe7-494a-98cf-bce2c6ea6c10","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.075946Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:b64a81318dea88692a7269e1e991b1713736964cb1908f843377918dd03ed8bd","observation_id":"74b5a2b1-5abd-412f-a551-eb3a800fb3db","resolution":{"observed_at":"2026-08-07T14:22:51.276750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.256226Z","title":"MetaSTNet: Multimodal meta-learning for cellular traffic conformal prediction,","venue":null,"work_id":"775402f9-08f1-426f-8751-74c5c8428750","year":1999},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.081535Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:68f7b8eda121425decf8a0f4ac84e01781b8356b1c0f52195349d5040674bd8d","observation_id":"9d96349c-8d30-4a81-b9ac-96eaa84c3f29","resolution":{"observed_at":"2026-08-07T14:22:51.261407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.241163Z","title":"TimeMixer: Decomposable multiscale mixing for time series forecasting,","venue":null,"work_id":"df04be0a-381c-4f7a-a424-119beeb180fc","year":2024},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.085993Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:0a2da63ee6b4bb4e0bf04be6a392aca15c4e40d27e9bb4df72e5de5c830c3d14","observation_id":"8adf6c02-78fa-4445-9e91-8e853d20a38a","resolution":{"observed_at":"2026-08-07T14:22:51.245915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.225404Z","title":"FedGRU: Privacy- preserving traffic flow prediction via federated learning,","venue":null,"work_id":"79212004-0849-43b4-a406-a0e9446e094b","year":2020},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.091766Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:17902cab4f1ed28840fef314e28d88eb175e3aba598768bf685d6c3bb0b3ae0e","observation_id":"4cb996d8-f922-48ed-bcb0-519e234c81e8","resolution":{"observed_at":"2026-08-07T14:22:51.230786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.209075Z","title":"Privacy-preserving traffic flow prediction: A federated learning approach,","venue":null,"work_id":"ffb7b746-7c4a-42b6-9083-fd41ca2f9cdd","year":2020},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.096548Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:b76d1698ca2ee2eb7b6792dfa2827e12b31224bbc5487959203547413b3635a5","observation_id":"a99960d9-f893-4d0e-8a09-c65518d67cc3","resolution":{"observed_at":"2026-08-07T14:22:51.214525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.191788Z","title":"Fed-NTP: A federated learning algo- rithm for network traffic prediction in V ANET,","venue":null,"work_id":"baa71ad8-7bc6-40d2-b45b-9f2cc381db46","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.101581Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:dd712c073827f98a5f76e2d8203cc432f3181c7af9f8c60d5fc09b7f50638617","observation_id":"c36baa79-5a59-4db3-91a3-3b9b99f07318","resolution":{"observed_at":"2026-08-07T14:22:51.196902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.173211Z","title":"Centralized and federated learning for predictive VNF autoscaling in multi-domain 5G networks and beyond,","venue":null,"work_id":"aae975e2-d328-47a4-be9e-dfbad5da02d3","year":2021},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.106105Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:4149bbc5849664d638f3c50f135c9310e932ca4ab19d1318fc8128a83bdaede2","observation_id":"1ffcb9cf-100f-4fe3-934b-e7f51c05d31c","resolution":{"observed_at":"2026-08-07T14:22:51.179481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.157836Z","title":"FASTGNN: A topological information protected federated learning approach for traffic speed forecasting,","venue":null,"work_id":"7e064e7f-e172-4a0e-be8f-2c811f0847e3","year":2021},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.111114Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:82bab6fc31ba7dfd23e4d55d1dcbdf91576bdea450070b256f2fa01c94db82f8","observation_id":"e36985a4-7e30-4f2e-9a4c-480d3375b2c1","resolution":{"observed_at":"2026-08-07T14:22:51.162846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.140742Z","title":"Short-term traffic flow prediction based on graph convolutional networks and federated learning,","venue":null,"work_id":"c9ba84e2-e8b7-428a-99e7-844cbde89b2b","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.115392Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:80b87f1bd38d4f56f591da6da7d73849a95aff6220a1cae2a097482e973b60b2","observation_id":"4a95e419-cd99-48b5-8588-5590f2d73b42","resolution":{"observed_at":"2026-08-07T14:22:51.146584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.123744Z","title":"Privacy-preserving cross-area traffic forecasting in ITS: A transferable spatial-temporal graph neural network approach,","venue":null,"work_id":"bdbe6019-fbe9-46cd-99f8-2ff65d041b0a","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.120883Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:60d2811b00d3b6c3e573e742980b6ed93795c58c877ec597377682de18e1cf86","observation_id":"0ad7fc4b-c922-4597-a404-6f8d37e07795","resolution":{"observed_at":"2026-08-07T14:22:51.129612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.126206Z","title":"Federated optimization in heterogeneous networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.126206Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:0d218a9b1598bcdd98319e5ca9f5685261c23529210c184a1d530930603bf122","observation_id":"cb4e3ed9-663d-4629-adc3-495736d87d06","resolution":{"observed_at":"2026-08-07T14:22:50.126206Z","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":"2026-08-07T14:22:51.098479Z","title":"Learning private neural language modeling with attentive aggregation,","venue":null,"work_id":"0f8eb6e9-1448-4e74-a55e-005e0f54a7b8","year":2019},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.131872Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:79c0e33fa5b738f786ab03e784d501923f14699f64bfaeb844da5bd498ca0ed3","observation_id":"be3f32a4-55d7-46f8-9f28-f693a9d06bff","resolution":{"observed_at":"2026-08-07T14:22:51.103483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.083525Z","title":"Dual attention- based federated learning for wireless traffic prediction,","venue":null,"work_id":"ad3c0016-0aaf-4ef9-a09d-e9aff6bfbf60","year":2021},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.136645Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:870a47ab13a66f3ee5f134b4bdb64dda14b93df32f14fe2a84cb672d53143537","observation_id":"7c8c15d7-268b-465e-8de7-612e5f9e4ecd","resolution":{"observed_at":"2026-08-07T14:22:51.088801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.068081Z","title":"Differential privacy for deep and federated learning: A survey,","venue":null,"work_id":"f29dc2d2-38c3-46dd-84f3-b64185f19599","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.141829Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:ce57738ab8198e9dcf158a4e8629a7b1f5626a7c9afb9d4112f6790eb90a8ddc","observation_id":"272d5a83-7d71-4e5f-8f52-5dcd9edc8ed1","resolution":{"observed_at":"2026-08-07T14:22:51.073813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.12254","last_updated":"2020-11-07T01:52:13Z","snapshot_observed_at":"2026-07-06T09:15:15.738005Z","submitted_at":"2020-04-25T23:47:25Z","title":"Privacy in Deep Learning: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.12254","snapshot_observed_at":"2026-08-07T14:22:50.146066Z","title":"Privacy in deep learning: A survey,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.146066Z"},"links":{"cited_paper":"/paper/2004.12254","citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:8b15d5537282f7fdb076278bff3360ab5f01e58b400bda9f61f4f21e5edec044","observation_id":"ec16a918-2572-4177-ac49-3389ce0d5d29","resolution":{"observed_at":"2026-08-07T14:22:50.146066Z","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":"2026-08-07T14:22:51.052694Z","title":"Privacy- preserving approach PBCN in social network with differential privacy,","venue":null,"work_id":"71e6d835-d84f-45b6-9f65-808084ed97c3","year":2020},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.150237Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:490a1dfd3e3766461c93628a03bd7d8623db51b669ae27aa88fe3ff0e1ddc60b","observation_id":"6d06e7fa-b958-4e7c-b8fb-e3c0b3588ed8","resolution":{"observed_at":"2026-08-07T14:22:51.057891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.01068","last_updated":"2024-10-23T20:45:38Z","snapshot_observed_at":"2026-08-05T01:26:35.677621Z","submitted_at":"2023-02-02T12:56:46Z","title":"FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.01068","snapshot_observed_at":"2026-08-07T14:22:50.154466Z","title":"Fed-GLOSS-DP: Fed- erated, global learning using synthetic sets with record level differential privacy,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.154466Z"},"links":{"cited_paper":"/paper/2302.01068","citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:4602f3c74e16bc446b34874bab6a3e5aab4024014775bd57fc09e642d228711c","observation_id":"86db1ef7-d72c-485a-9af1-6d8eee3cdd9f","resolution":{"observed_at":"2026-08-07T14:22:50.154466Z","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":"2026-08-07T14:22:51.038494Z","title":"Performance-enhanced federated learning with differential privacy for Internet of things,","venue":null,"work_id":"dca6cdc0-6b23-41cc-9d6e-9979ff3f30d0","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.159194Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:c50cd7007bf0292fd1db40910ad0107066c0b3b1cc0ad47b9ca95934d424fc4f","observation_id":"af45b8d1-08ce-4021-9d91-dcd91b6f4a98","resolution":{"observed_at":"2026-08-07T14:22:51.043171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.022825Z","title":"Federated learning with differential privacy: Algorithms and performance analysis,","venue":null,"work_id":"e3eb1700-e4c7-4e14-bbe6-dc5c082370e6","year":2020},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.163868Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:e68165a0161756de1e1f27d0dd454215610ee88206f4687326740161e3117554","observation_id":"fbf7410c-f4f1-4d5a-9a0b-33bd1ee44672","resolution":{"observed_at":"2026-08-07T14:22:51.027962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:51.006420Z","title":"Personalized federated learning with differential privacy,","venue":null,"work_id":"1604fe8a-a6ac-474d-8841-40742ce498dc","year":2020},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.168823Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:a11d3c9c2d68918b902702f9d81b708e316968314923284dffa4239923570d1c","observation_id":"fb3a9450-8624-47ab-ae82-9718b76e2669","resolution":{"observed_at":"2026-08-07T14:22:51.011915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.991262Z","title":"Towards efficient and privacy-preserving federated deep learning,","venue":null,"work_id":"b8ecd6c4-4d9c-44b4-a1c3-cfc9858a3c22","year":2019},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.173592Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:fa1369c2d46eece3443eddb31182ba6550e94112b22e1ecd88a84124562170ae","observation_id":"510c0384-57ee-4451-b3a0-e67e59d872ba","resolution":{"observed_at":"2026-08-07T14:22:50.996256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.976356Z","title":"On differential privacy for federated learning in wireless systems with multiple base stations,","venue":null,"work_id":"305eb260-d9fb-432f-815e-221a6b974c16","year":2024},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.178642Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:84ebc355ec08b5794aa8206c04ff935093f1d159015cf21ba9a252b17fa0ca7d","observation_id":"73517ffe-09cf-4edd-9937-dea89c9774b5","resolution":{"observed_at":"2026-08-07T14:22:50.981276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.961959Z","title":"GRNN: Generative regression neural network—a data leakage attack for federated learning,","venue":null,"work_id":"2e75295c-f8d9-4013-b3a4-37ffc15d4782","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.183978Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:114b0eacccda48c3b903a5167a1cc81d575971bae64ff1fd430e6ca4c983ed4e","observation_id":"07911358-9adb-494a-8847-8995aefa299e","resolution":{"observed_at":"2026-08-07T14:22:50.966496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.946819Z","title":"Byzantine machine learning: A primer,","venue":null,"work_id":"9af961fa-9f38-450c-a1e7-b7a719571bc5","year":2023},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.188947Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:d8efda8a5bd6dbd7f6dc80a910a10f8ebf7693027b2bef8766e01fead4dc92a9","observation_id":"3881c8c1-5c1f-47f8-a97a-2f178e338766","resolution":{"observed_at":"2026-08-07T14:22:50.951941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.930097Z","title":"Byzantine fault tolerance in distributed machine learning : a survey,","venue":null,"work_id":"fc6b56f9-5d21-4fa6-bdb5-fd1b5d170bd3","year":2024},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.193851Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:684014cdcbb94ba7e0411308a4c7458931f8a8285d10ccd2d356bd232cd41956","observation_id":"e8694dd1-5ac1-4c03-a185-b84b1617b85b","resolution":{"observed_at":"2026-08-07T14:22:50.936175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.198828Z","title":"An experimental study of Byzantine-robust aggregation schemes in federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.198828Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:d0f124d474eab3c0a514823e65dbf82bba66c0028decec0a76ed8ff444dedca6","observation_id":"08c6b61c-c82b-40fb-bb6c-a376c2e1dfba","resolution":{"observed_at":"2026-08-07T14:22:50.198828Z","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":"2026-08-07T14:22:50.914829Z","title":"DRACO: byzantine-resilient distributed training via redundant gradients,","venue":null,"work_id":"f9cfce33-eda3-40e4-9e5c-efcc2f7b29c3","year":2018},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.204030Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:5aa5f30946b0fbedf73402b6aba09224ffba9a9f6edafb831280fd74ffd4d614","observation_id":"8a8f3417-33f1-44fa-b776-5ea39a14b8c9","resolution":{"observed_at":"2026-08-07T14:22:50.919719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.900106Z","title":"DETOX: A redundancy-based framework for faster and more robust gradient aggregation,","venue":null,"work_id":"d9cefbb8-7520-490a-9832-bd91c6535d38","year":2019},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.209680Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:168a3382f26841684f2b5e976b8fdfd20888e50807856017ee5f9c9464d5818a","observation_id":"4bd16110-e009-4753-8657-8f2ee6c6975c","resolution":{"observed_at":"2026-08-07T14:22:50.905289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.885509Z","title":"FLTrust: Byzantine-robust federated learning via trust bootstrapping,","venue":null,"work_id":"fec0c905-2261-40e8-910b-c757cf7f3a54","year":2021},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.214449Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:0913a9da5d74df6f285e2e1fd88330f3b6e72ee727217d06e7bc5b23c5599bbd","observation_id":"4f44420e-94c8-4fea-93c7-3e1d7ac7ec60","resolution":{"observed_at":"2026-08-07T14:22:50.889892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.870358Z","title":"Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,","venue":null,"work_id":"bea0fc4b-9c2b-4305-bcb9-b2d680c05b57","year":2017},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.219607Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:8b5317a9129eaee571f9c672db3a8aba202e2fbb403464e9ffc1ecae656478af","observation_id":"39d84078-cacc-4b3f-9643-79d8bb16c5c4","resolution":{"observed_at":"2026-08-07T14:22:50.875373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.855502Z","title":"Byzantine-robust aggregation in federated learning empowered industrial IoT,","venue":null,"work_id":"fb752c0e-b38c-4afd-961f-18c790a427a8","year":2023},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.224638Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:241adf167a660e9b82a9917a7c3f6ff6c6c669cdcfc19cbcadee7ff3707e7d29","observation_id":"88145af5-7e61-4039-bb84-2cf7cff3bb85","resolution":{"observed_at":"2026-08-07T14:22:50.860451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.840264Z","title":"Learning from history for Byzantine robust optimization,","venue":null,"work_id":"247758c8-b2a1-4839-b0d5-a941153291a0","year":2021},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.229846Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:34fa15451c017c6ee8d8ef2640500645d516a7320f155909dbbeb34d3f98b6df","observation_id":"70d55bb2-3e3a-4c6b-bfda-2208319a59df","resolution":{"observed_at":"2026-08-07T14:22:50.845070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.825613Z","title":"Distributed distributionally robust optimization with non-convex objectives,","venue":null,"work_id":"38211f32-5078-4c55-9f28-828c0947fc54","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.234403Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:989b4a07cbdb5e756e28fc1832dabc167a614684e338bb59829799aa1f7b58c7","observation_id":"041f6923-9de1-49db-aac1-db6c99a6f1da","resolution":{"observed_at":"2026-08-07T14:22:50.830640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.11719","last_updated":"2023-07-09T21:53:45Z","snapshot_observed_at":"2026-08-07T21:40:40.556424Z","submitted_at":"2022-07-24T11:23:31Z","title":"Gradient-based Bi-level Optimization for Deep Learning: A Survey","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.11719","snapshot_observed_at":"2026-08-07T14:22:50.238544Z","title":"Gradient-based bi-level optimization for deep learning: A survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.238544Z"},"links":{"cited_paper":"/paper/2207.11719","citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:949c9dcab57da03c8b7b126e5c33db2bd666afcbf74737aecb95150cd874dc3a","observation_id":"ccc06cb3-b767-4221-bfb0-141fc500e778","resolution":{"observed_at":"2026-08-07T14:22:50.238544Z","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":"2026-08-07T14:22:50.809411Z","title":"Fair resource allocation in federated learning,","venue":null,"work_id":"e83d6c45-1d77-459e-a9b9-9f4843e7e511","year":2020},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.243404Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:92507b56f8dde9517d5cc0b38630d84795277dd335c1adf61e2685b1a3074bbc","observation_id":"12da4457-5050-47c0-a995-ae828936f2e1","resolution":{"observed_at":"2026-08-07T14:22:50.815366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.794723Z","title":"Asynchronous distributed bilevel optimization,","venue":null,"work_id":"b81478c6-13bc-4d87-bf02-a3961ce52a10","year":2023},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.248882Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:e76910aa88a3aaef5581d9f0b4334bf1dd451c99575f7358c2ad3524f9e21f46","observation_id":"0e2a2919-b3e7-4acd-a1f1-0e331612281d","resolution":{"observed_at":"2026-08-07T14:22:50.799162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.779632Z","title":"Agnostic federated learning,","venue":null,"work_id":"72c34bd5-2057-4658-b50a-6b39fd92d6fc","year":2019},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.253408Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:a866890ab9bd5ec7c22b5b031d4fbf88852ecc0a25694ea31de11ac5ecf79948","observation_id":"f088d6b8-b358-43e5-b11f-8b7eab7868be","resolution":{"observed_at":"2026-08-07T14:22:50.784172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.763326Z","title":"Distributionally robust federated averaging,","venue":null,"work_id":"83ff60fe-cce7-4b2a-bc30-2530f86dfffb","year":2020},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.258329Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:a5e822fb303f3a93f093dc4f0c739b3501695bf93e55a101f65fe50933d59f0f","observation_id":"74fbef67-88bc-4c01-ab80-e7b7468d1ba1","resolution":{"observed_at":"2026-08-07T14:22:50.768667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.745186Z","title":"Data-driven distributionally robust op- timization using the Wasserstein metric: Performance guarantees and tractable reformulations,","venue":null,"work_id":"c5ca2d10-a22c-4399-b855-4d9fd37fec1c","year":2017},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.262606Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:1788f7052576d15a1d55b617b62b014de7dc8e0a2ba488a5ddf4acb5f5070cbf","observation_id":"714c2c79-7023-41b4-b180-0a6bbe35959c","resolution":{"observed_at":"2026-08-07T14:22:50.751453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.728592Z","title":"On the rate of convergence in Wasserstein distance of the empirical measure,","venue":null,"work_id":"ed16676a-f3aa-4386-910a-f6e1292b47b0","year":2015},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.267702Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:7f504eb8d725120ce321ba17ea3dbcfd5a7fa4c9e011cd349d4fe2dce8a86d89","observation_id":"7b937606-a4d0-42f5-bc5c-ce5377b490a5","resolution":{"observed_at":"2026-08-07T14:22:50.734298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.712117Z","title":"Distributionally-robust machine learning using locally differentially-private data,","venue":null,"work_id":"7f258ae8-aa98-46f3-91fb-4dd6adcd9a1e","year":2022},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.272702Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:e29d8060df9f4feee0a146ef84c2d9433c2a68b03b7b8215231f25b3e9366e3e","observation_id":"f719ec10-59b9-4316-bfa5-fba8e68c2fec","resolution":{"observed_at":"2026-08-07T14:22:50.716764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.697754Z","title":"Bilevel optimization: Convergence analysis and enhanced design,","venue":null,"work_id":"cd768b58-6c83-4586-a591-059532d13f3d","year":2021},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.277645Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:c9e4469d122309e56833e067a56a0f6b79fbb9b0f53ea96f81666dd4d431ca9c","observation_id":"5d1ee27d-e996-43ce-958d-d6e925151ecd","resolution":{"observed_at":"2026-08-07T14:22:50.702250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.682563Z","title":"Convergence of meta-learning with task-specific adaptation over partial parameters,","venue":null,"work_id":"61813119-771f-47cb-b489-a964e2f736f8","year":2020},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.282285Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:4614c98e7dadc47086d3c9a8c2464de8dcf4416871dd38738d4049e3b28d4f11","observation_id":"c834a373-dd32-4014-92b1-cffdf6311dd0","resolution":{"observed_at":"2026-08-07T14:22:50.687662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.667542Z","title":"The suplementary content,","venue":null,"work_id":"01032e6f-4d01-46fb-9c38-971005234ff7","year":2025},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.287547Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:3ddd8acd631a84396699d9c9863dc1d17f3a40cfe558673c4cb3f3276fd3849a","observation_id":"772c2e1e-bec7-42f8-9f2d-76eba2287518","resolution":{"observed_at":"2026-08-07T14:22:50.672825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.292591Z","title":"Telecommunications - SMS, Call, Internet - MI,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.292591Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:ab47b14854698008f0455c1a6d34e52d1678617197c2dd3a96391b9ab7fc035c","observation_id":"bd423b32-9c51-4ef0-96f0-ab1d24e4416b","resolution":{"observed_at":"2026-08-07T14:22:50.292591Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:22:50.297672Z","title":"Social Pulse - Milano,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.297672Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:594d34b49cb770372a784d17d804f2a185c7213958c3472cdf45095150050437","observation_id":"71142aa4-98a8-4929-86bc-9da16880f6a7","resolution":{"observed_at":"2026-08-07T14:22:50.297672Z","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":"2026-08-07T14:22:50.651654Z","title":"MilanoToday,","venue":null,"work_id":"7b59b742-c464-449b-a7be-4db2e6b61fca","year":2015},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.302541Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:c9e46151cd8d059668530cace6ac176549e643b4bf1fe488d09a8fcb025624a0","observation_id":"ef54c9f6-79ca-4cbf-bac4-3c5579502d46","resolution":{"observed_at":"2026-08-07T14:22:50.656696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:50.307635Z","title":"Telecommunications - SMS, Call, Internet - TN,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.307635Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:b364cf4c9bde19dbb0161c5abbc4583c4daea85003e0f70630dfe426dfd308ae","observation_id":"9cc65245-c8b1-4dce-8753-82006379a28b","resolution":{"observed_at":"2026-08-07T14:22:50.307635Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:22:50.312715Z","title":"Social Pulse - Trentino,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.312715Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:5d1c09c782fdca61312eb5d97a242ecb319e4957ed8d994566961598c6fd9144","observation_id":"fbbbeee8-2a40-4e62-be4e-73e1fe33d01a","resolution":{"observed_at":"2026-08-07T14:22:50.312715Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:22:50.317726Z","title":"TrentoToday,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.317726Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:15f6f076b65f99bd5f2e21daae5607c04d9f55ac865eac410f5cb24e0d035266","observation_id":"c793023c-3688-4b07-8dca-f4287073560e","resolution":{"observed_at":"2026-08-07T14:22:50.317726Z","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":"2026-08-07T14:22:50.624148Z","title":"User- level privacy-preserving federated learning: Analysis and performance optimization,","venue":null,"work_id":"bc8dd359-f90d-453e-94e5-dc0791d2b7f0","year":2021},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:50.322024Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:537d46eef7bce4646b51333472a5f152ce5769d4eb4f6a0cafcc44d0ec697cba","observation_id":"cc367ffb-b5e5-4308-92c5-0575f8f91ce1","resolution":{"observed_at":"2026-08-07T14:22:50.630954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T14:22:49.944467Z","title":"Available: https://proceedings.neurips.cc/paper/2017/ hash/f4b9ec30ad9f68f89b29639786cb62ef-Abstract.html","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning","version":1},"reference_index":129,"source":"pdf_text","source_observed_at":"2026-08-07T14:22:49.944467Z"},"links":{"citing_paper":"/paper/2505.19263"},"observation_digest":"sha256:e1541aefcd5a1be75c9d7075646370f1f6e876d6a92b96561b4a67eaa3c8e03c","observation_id":"6b2fe8ff-e489-47be-9634-e5d39c233671","resolution":{"observed_at":"2026-08-07T14:22:49.944467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.19263","last_updated":"2025-05-25T18:38:57Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T14:15:45.275806Z","submitted_at":"2025-05-25T18:38:57Z","title":"Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":64},"total_outbound_references":75},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2505.19263."}