{"as_of":"2026-08-11T01:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:506c15ab3f4b8d24e2c3a0d0ba7cd3ce7e565cfa18bf4d9312c136e408a41292","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T22:28:55.469518Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2511.10502/citation-record","integrity":"/paper/2511.10502/integrity","json":"/paper/2511.10502/citation-record.json","paper":"/paper/2511.10502"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T22:28:49.963529Z","title":"When machine learning meets privacy: A survey and outlook,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:49.963529Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:4bf1a2b5a0695968c21a519148c3841135ee7d99dfcbff2322f94dfdc533df68","observation_id":"da6079db-30c5-4d48-8f23-8211f458764d","resolution":{"observed_at":"2026-08-03T22:28:49.963529Z","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":"2026-08-03T22:28:50.059327Z","title":"Communication-efficient learning of deep networks from decentral- ized data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:50.059327Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:7f60f1982601fd2bb84e212f877abc12acd7776d9e10fdde1e5498530e3ea035","observation_id":"6baf34ea-b237-4ac1-9a5d-99b7e5e3f89c","resolution":{"observed_at":"2026-08-03T22:28:50.059327Z","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":"2026-08-03T22:28:50.288367Z","title":"Decentralised Learning in Federated Deployment Environments: A System-Level Survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:50.288367Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:dc1e34365d63b6eaff0ee4db229d1934b006487420c8bf1b124b5ba5145f0ff2","observation_id":"9d68cdbf-5619-4b34-99a5-a7fc8236f5bb","resolution":{"observed_at":"2026-08-03T22:28:50.288367Z","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":"2026-08-03T22:28:50.545390Z","title":"Sok: Gradient inversion attacks in federated learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:50.545390Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:8671106261a9049e9090b69f39c66f095a90c53d693678052d4985333b7fa6e1","observation_id":"52e64af3-0afd-43bb-9d26-5382059e7514","resolution":{"observed_at":"2026-08-03T22:28:50.545390Z","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":"2026-08-03T22:28:50.719372Z","title":"Sok: Gradient leakage in federated learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:50.719372Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:c34a3d6651486a969e567f7afc426a69c2a07e4af35189d8e79dd55021f48ca7","observation_id":"10053233-8bef-48ed-9beb-b6da43dfbbb3","resolution":{"observed_at":"2026-08-03T22:28:50.719372Z","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":"2026-08-03T22:28:50.861810Z","title":"Hiding in plain sight: Disguising data stealing attacks in federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:50.861810Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:f9904bb564116f75b7d7afc98a796f33a4f33d2c4e18963b775b9a0b26a7dc40","observation_id":"feeb1175-bbe0-4a2d-88df-dc434744e20d","resolution":{"observed_at":"2026-08-03T22:28:50.861810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.20651","last_updated":"2025-06-25T17:49:26Z","snapshot_observed_at":"2026-08-09T00:10:42.383708Z","submitted_at":"2025-06-25T17:49:26Z","title":"Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.20651","snapshot_observed_at":"2026-08-03T22:28:50.979969Z","title":"Hear no evil: Detecting gradient leakage by mali- cious servers in federated learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:50.979969Z"},"links":{"cited_paper":"/paper/2506.20651","citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:bddcf8f748f9091e5da9427c1ff1f58052f9cbe91aac194d11b24b582bbcd9c3","observation_id":"cf9e38d9-032a-4949-8e64-21e37890ad7f","resolution":{"observed_at":"2026-08-03T22:28:50.979969Z","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":"2026-08-03T22:28:51.138017Z","title":"Robbing the fed: Directly obtaining private data in federated learning with modified models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:51.138017Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:51ef20864c5296b799fc2def90511d081bfc3879b8967df6b5b8664058286b10","observation_id":"36c076b9-6048-44c2-a890-f1f33c5f996e","resolution":{"observed_at":"2026-08-03T22:28:51.138017Z","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":"2026-08-03T22:28:51.365911Z","title":"Loki: Large-scale data reconstruction attack against federated learning through model manipulation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:51.365911Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:e264061f8979b6a25d4f5c17b322f700ffa7c09d9a102de9e7b27d9d8a83acf5","observation_id":"8fa3e384-c46b-4253-b58d-9597a96d4856","resolution":{"observed_at":"2026-08-03T22:28:51.365911Z","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":"2026-08-03T22:28:51.622657Z","title":"Fishing for user data in large-batch federated learning via gradient magnification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:51.622657Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:512b355399214676ba23d4c4c11b1eeffe3aa394884f6d89d8d27abe20b8dd21","observation_id":"fb5da412-a9a1-4698-a599-a35f2119e59a","resolution":{"observed_at":"2026-08-03T22:28:51.622657Z","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":"2026-08-03T22:28:51.772318Z","title":"When the curious abandon honesty: Fed- erated learning is not private,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:51.772318Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:be081ae6e184ac6dca2e3f640c3aa3c24fb2623e9c4487ad55d2ffb74cc0fc36","observation_id":"f5074a24-197f-48e8-b4a7-d0eb15e0f958","resolution":{"observed_at":"2026-08-03T22:28:51.772318Z","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":"2026-08-03T22:28:51.932317Z","title":"Reconstructing individual data points in federated learning hardened with differential privacy and secure aggregation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:51.932317Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:63cb9c82da50e0557e93d22633ea4552738bf6ab31c1c8bcff93ee0411266f88","observation_id":"f7485c15-e0d7-4bfb-a05f-428f778faa3d","resolution":{"observed_at":"2026-08-03T22:28:51.932317Z","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":"2026-08-03T22:28:52.104615Z","title":"Maximum knowledge orthogonality reconstruction with gradients in federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:52.104615Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:ad343707eedbde53691e66c8be77ee4fde6f157ef66dcd66f85beaaa18197642","observation_id":"a40bbfcc-981d-4008-8ba3-267ab9b79924","resolution":{"observed_at":"2026-08-03T22:28:52.104615Z","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":"2026-08-03T22:28:52.212507Z","title":"Scale-mia: A scalable model inversion attack against secure federated learning via latent space reconstruction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:52.212507Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:3c5da207502a73f24c02307ad44b94750ebf9ff8065456f055b10f581105a578","observation_id":"c6808906-bfd1-4848-9217-ee247c009c49","resolution":{"observed_at":"2026-08-03T22:28:52.212507Z","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":"2026-08-03T22:28:52.378362Z","title":"Geminio: Language-guided gradient inversion attacks in federated learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:52.378362Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:126d182b04b10fb6de9c4ecb040af3b1792e7f1eaf3cf13268360241957d117a","observation_id":"9e4f813d-52dd-4d75-8927-3f87654a3573","resolution":{"observed_at":"2026-08-03T22:28:52.378362Z","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":"2026-08-03T22:28:52.500347Z","title":"Deep leakage from gradients,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:52.500347Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:63e24a5951b122220f5823121841b21987cbad7849161076f1e27f44646a6a0b","observation_id":"b456e3cc-6e35-44f5-973d-be05ed0d5e8c","resolution":{"observed_at":"2026-08-03T22:28:52.500347Z","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":"2026-08-03T22:28:52.618469Z","title":"Inverting gradients - how easy is it to break privacy in federated learning?","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:52.618469Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:82b9a2d145caaf8e325c9d8e02b5a222bd9c29073797924b4cbfc1771e7b4e11","observation_id":"c260ebc1-df14-4b8a-9658-9bb1fc1a704e","resolution":{"observed_at":"2026-08-03T22:28:52.618469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.14390","last_updated":"2022-03-05T20:30:32Z","snapshot_observed_at":"2026-07-06T09:42:35.058716Z","submitted_at":"2020-07-28T17:59:07Z","title":"Flower: A Friendly Federated Learning Research Framework","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.14390","snapshot_observed_at":"2026-08-03T22:28:52.758453Z","title":"Flower: A friendly federated learning research framework,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:52.758453Z"},"links":{"cited_paper":"/paper/2007.14390","citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:d8db11f8f7afe02415c13ce40d6f585cb75478e02c191e64ab4a2c7d4b6970de","observation_id":"b9d1fbf8-886e-472f-9d26-99050d84a72f","resolution":{"observed_at":"2026-08-03T22:28:52.758453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.06335","last_updated":"2019-09-13T17:26:20Z","snapshot_observed_at":"2026-08-02T11:40:53.964079Z","submitted_at":"2019-09-13T17:26:20Z","title":"Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.06335","snapshot_observed_at":"2026-08-03T22:28:52.926280Z","title":"Measuring the effects of non- identical data distribution for federated visual classification,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:52.926280Z"},"links":{"cited_paper":"/paper/1909.06335","citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:4a484117806d2adc1cd2deb70aee1f30be20c33ecf7fa895fc150dfea385ba64","observation_id":"bef96c45-6109-496d-ad8c-0ff9a4446836","resolution":{"observed_at":"2026-08-03T22:28:52.926280Z","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":"2026-08-03T22:28:53.054400Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:53.054400Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:a672cbf5594919635c9c6c82cca417cec0cf4db567e2560af5b257da1cd13d24","observation_id":"135f20f7-7179-4f9f-9c0b-46a3b12f3458","resolution":{"observed_at":"2026-08-03T22:28:53.054400Z","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":"2026-08-03T22:28:53.197048Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:53.197048Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:5480de10df1778b4693a222f28a506f7b6d40612d05330b2b1c91c6e3f760615","observation_id":"e981d35e-ea91-44f8-adb3-fee67f24ef59","resolution":{"observed_at":"2026-08-03T22:28:53.197048Z","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":"2026-08-03T22:28:53.308937Z","title":"The mnist database of handwritten digit images for machine learning research [best of the web],","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:53.308937Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:7fc78138947caa06963550c0689a39a0c207b2e171c85d509f1f20fe5ec51cec","observation_id":"8d4e30bc-c7e1-455a-a3a6-9518d4203740","resolution":{"observed_at":"2026-08-03T22:28:53.308937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-07-06T05:56:41.814255Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-03T22:28:53.418826Z","title":"Fashion-mnist: a novel im- age dataset for benchmarking machine learning algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:53.418826Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:3652e4583d79290499424ed65dbec5c4c2d76434128c4056d9f340a54ceea72d","observation_id":"9cdc2419-056f-42ac-87de-5dbb960d6007","resolution":{"observed_at":"2026-08-03T22:28:53.418826Z","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":"2026-08-03T22:28:53.480735Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:53.480735Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:fffc8759a2e1e3afa222484a021b270617d3119fda85c8c3be8c53111d47e7e4","observation_id":"ff4a2be9-a288-4b95-88c0-e91fcb6f56a2","resolution":{"observed_at":"2026-08-03T22:28:53.480735Z","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":"2026-08-03T22:28:53.641085Z","title":"Gradient-based learning applied to document recognition,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:53.641085Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:6ed3cea1aa34336df135bdd9946eb30c82e958715e5bb09ac078b18edb0a5714","observation_id":"0d04d501-4447-4b05-a791-f9c0b63551ca","resolution":{"observed_at":"2026-08-03T22:28:53.641085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-10T22:24:05.831832Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-03T22:28:53.800898Z","title":"Very deep convolutional networks for large-scale image recognition,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:53.800898Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:51823a523f968866ead4da3324b7ff509d8ed008bc5fc91b06e73bb1c46dbc6c","observation_id":"e0f6e919-0e21-41eb-ba86-6a64c939e432","resolution":{"observed_at":"2026-08-03T22:28:53.800898Z","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":"2026-08-03T22:28:53.968777Z","title":"The resource problem of using linear layer leak- age attack in federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:53.968777Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:cb135a0b80b99f0bd43efd9e10797aa00e10206b15c6a326da1f97978f2b0ca1","observation_id":"f67da76c-3d66-402c-9e90-8de805f5d41f","resolution":{"observed_at":"2026-08-03T22:28:53.968777Z","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":"2026-08-03T22:28:54.134730Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:54.134730Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:2f93713bfdb58924271dc16ab1a662abccce60ade82c52e8b0057b449a01c0f7","observation_id":"4dfb6da5-d7b6-4c55-97e9-ee00f0d747af","resolution":{"observed_at":"2026-08-03T22:28:54.134730Z","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":"2026-08-03T22:28:54.311488Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:54.311488Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:8a3677e816f1fcc388a0fa4134063fdd16cb4d9662cbd73aad1fa73dfb452bf8","observation_id":"06e83374-30a9-48f0-a7a9-6775dc8fcac7","resolution":{"observed_at":"2026-08-03T22:28:54.311488Z","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":"2026-08-03T22:28:54.472899Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:54.472899Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:1f27796d7273bed3c1c6145519f3716e74afbf46a5bc35a2acd099aa14b9a23c","observation_id":"c6a82e72-208c-4708-9daf-635709595434","resolution":{"observed_at":"2026-08-03T22:28:54.472899Z","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":"2026-08-03T22:28:54.805316Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:54.805316Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:7dc8499c09ebfc141714be07c3a2d1d041d8db29df3ee3e2735e949c0912b050","observation_id":"b7e1d709-cfde-438b-a637-3aa42c8eecb5","resolution":{"observed_at":"2026-08-03T22:28:54.805316Z","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":"2026-08-03T22:28:54.917375Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:54.917375Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:2237bff5553b614d648b32485cd8490c2aa26398d455844c9155c553d95f72b7","observation_id":"c4ce71cd-1a04-4ed7-ad1c-91ffcfb1ae39","resolution":{"observed_at":"2026-08-03T22:28:54.917375Z","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":"2026-08-03T22:28:55.029181Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:55.029181Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:af6c4cd1aef429bce6916dce9e1d6c501940f7637b800708f67480d43ff67d9e","observation_id":"62081286-2eb0-48f0-ae98-7905c3b5fb03","resolution":{"observed_at":"2026-08-03T22:28:55.029181Z","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":"2026-08-03T22:28:55.195136Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:55.195136Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:b7bca6d366142f64ba13f1192c356595fb55cf9b643c8299373807dd85302016","observation_id":"39eb687d-eb68-4443-8182-bd26c6dade6f","resolution":{"observed_at":"2026-08-03T22:28:55.195136Z","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":"2026-08-03T22:28:55.359373Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:55.359373Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:855aa7676a7ef5bfef540af2d41ca7542fb529e8104f374e842669252244b301","observation_id":"70d881db-77de-4b7d-8f9d-2a4b99116d42","resolution":{"observed_at":"2026-08-03T22:28:55.359373Z","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":"2026-08-03T22:28:55.469518Z","title":"All experiments simulate an IID data distribution","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T22:28:55.469518Z"},"links":{"citing_paper":"/paper/2511.10502"},"observation_digest":"sha256:5a530fd59fc557cfa6927b46502b16eb313a4f4d14af4abbab586f2b0e1326c7","observation_id":"bbe31d1c-b025-46b6-bcea-ffc05c481cda","resolution":{"observed_at":"2026-08-03T22:28:55.469518Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2511.10502","last_updated":"2025-11-13T17:06:57Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-08T16:44:39.778451Z","submitted_at":"2025-11-13T17:06:57Z","title":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":35,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":36},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2511.10502."}