{"as_of":"2026-08-12T20:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9404e44f5f6097e6de0f2b19f5daa68ad190c59c730b4c33b33a6227c2345027","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T09:13:22.918574Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2607.20890/citation-record","integrity":"/paper/2607.20890/integrity","json":"/paper/2607.20890/citation-record.json","paper":"/paper/2607.20890"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T09:13:16.627827Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:16.627827Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:2ece7f44eacbcbbf8248b9249816da3e2734008277e9dd761f29be7513b04eb8","observation_id":"fa63545c-73cc-404b-b206-df65135636f7","resolution":{"observed_at":"2026-08-01T09:13:16.627827Z","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":"2026-08-01T09:13:16.763670Z","title":"Federated learning: Challenges, methods, and future directions,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:16.763670Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:89f0d2cc0ca0d4d88c26564c5a0f0e50b4da245c2b4548e7690cf995244ee706","observation_id":"d0dddae5-a233-42fd-959b-90b95ee1276a","resolution":{"observed_at":"2026-08-01T09:13:16.763670Z","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":"2026-08-01T09:13:16.844208Z","title":"Toward on-device federated learning: A direct acyclic graph-based blockchain approach,","venue":null,"work_id":null,"year":2028},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:16.844208Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:f59cdeaf8b9e5927d4b662140799837564e012720c8a98f20d81f2adde371d3f","observation_id":"e47254d9-a5fb-4035-b55e-78b4cd9092bd","resolution":{"observed_at":"2026-08-01T09:13:16.844208Z","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":"2026-08-01T09:13:16.908492Z","title":"Fedaux: Leveraging unlabeled auxiliary data in federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:16.908492Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:fb71113624ce1f97dbb90af58594b26d77fff755cc26870e9811cc99e22aeef5","observation_id":"8bd00087-9d00-4551-859e-9a15a387770f","resolution":{"observed_at":"2026-08-01T09:13:16.908492Z","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":"2026-08-01T09:13:16.966919Z","title":"Active client selection for clustered federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:16.966919Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:a1d9f35319bfb105463892e6bb874259f48844b6d0a9f171f894add5c129556b","observation_id":"c6baebcc-3b8a-4651-8989-fb45a065d897","resolution":{"observed_at":"2026-08-01T09:13:16.966919Z","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":"2026-08-01T09:13:17.045673Z","title":"Federated learning with taskonomy for non-iid data,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.045673Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:922b0c464090e4a480653a2bbcd2778d145810a54610ca5e0ab5e0433fe34dbf","observation_id":"9dca5fa0-86b4-4066-b16f-2368d1971bc3","resolution":{"observed_at":"2026-08-01T09:13:17.045673Z","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":"2026-08-01T09:13:17.130593Z","title":"Practical and robust federated learning with highly scalable regression training,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.130593Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:3bd0be9316d7a516e73316b240ba81a124e421864e0afbeb2e4a70c75f47c322","observation_id":"1a0b59ee-9437-4059-bdbc-88023f0812cf","resolution":{"observed_at":"2026-08-01T09:13:17.130593Z","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":"2026-08-01T09:13:17.177188Z","title":"Personalized federated graph learning on non-iid electronic health records,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.177188Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:3edee97ecb2f0e8fd3d8533ef626c39c00a26baf5abbbd5e82921d69c624ea8a","observation_id":"16cf0455-0916-4b17-875f-92f757f4e1c6","resolution":{"observed_at":"2026-08-01T09:13:17.177188Z","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":"2026-08-01T09:13:17.235937Z","title":"Clustered federated learning in het- erogeneous environment,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.235937Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:0e63bf44fe146e4d63160789bdf3acc1fa72aea8d8a9f2667c896559fe9b1577","observation_id":"fe3522a0-c610-4aea-b9dd-7e966bd01347","resolution":{"observed_at":"2026-08-01T09:13:17.235937Z","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":"2026-08-01T09:13:17.325271Z","title":"Communication-efficient randomized algorithm for multi-kernel online federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.325271Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:668f297e301542adfa2a3740efc8830b666c987df6dc6956c3c756a817fab39c","observation_id":"e6762d96-95c8-4006-ae34-15f57d620785","resolution":{"observed_at":"2026-08-01T09:13:17.325271Z","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":"2026-08-01T09:13:17.381942Z","title":"Tighter regret analysis and optimization of online federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.381942Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:c49c7ff2c0dd73fa51cd4a52d14c19089f773d7f12575416d0b50dbc205892ad","observation_id":"c98a2120-a9cf-4060-9ab1-4347fee63ab7","resolution":{"observed_at":"2026-08-01T09:13:17.381942Z","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":"2026-08-01T09:13:17.450581Z","title":"Federated learning in mobile edge networks: A comprehensive survey,","venue":null,"work_id":null,"year":2031},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.450581Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:5df0464d27a5f31f2a8060e8444baa7410fc284e85f0b1bdf648699e9ca9a40e","observation_id":"37704a54-f1d3-4b0a-ba7c-acaee707f56b","resolution":{"observed_at":"2026-08-01T09:13:17.450581Z","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":"2026-08-01T09:13:17.527602Z","title":"Advances and open problems in federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.527602Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:e3ae4cf937b916e78145af1706e3b852a9cb48fad9d90c8710227a02e2275f7f","observation_id":"adf3e880-f773-480a-bf38-3aa6e734d295","resolution":{"observed_at":"2026-08-01T09:13:17.527602Z","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":"2026-08-01T09:13:17.575996Z","title":"signSGD: Compressed optimisation for non-convex problems,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.575996Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:543eb633d65544e892c43703ffe673e26d2bbfe9faaa9ccc56442c132e4b2d17","observation_id":"29c1451a-a2f9-40cb-9c1e-cbcf7f596119","resolution":{"observed_at":"2026-08-01T09:13:17.575996Z","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":"2026-08-01T09:13:17.738655Z","title":"1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.738655Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:bc58b19c1aa234c1d17c3e8c0b5fca6c06a1acc919d25fe9d61ad3d84512babe","observation_id":"819b52ab-b76d-4622-86ca-e23b5f67b949","resolution":{"observed_at":"2026-08-01T09:13:17.738655Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05291","last_updated":"2019-02-22T19:55:48Z","snapshot_observed_at":"2026-07-06T07:07:34.689643Z","submitted_at":"2018-10-11T23:50:32Z","title":"signSGD with Majority Vote is Communication Efficient And Fault Tolerant","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05291","snapshot_observed_at":"2026-08-01T09:13:17.922587Z","title":"signsgd with majority vote is communication efficient and fault tolerant,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:17.922587Z"},"links":{"cited_paper":"/paper/1810.05291","citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:d515717d5379b930b2c19aaf5b6551c15f3c08e4f61c578f824ce5e5cb943b19","observation_id":"b8b543c5-391d-4df4-9a89-16638b226889","resolution":{"observed_at":"2026-08-01T09:13:17.922587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.07475","last_updated":"2023-02-15T05:36:41Z","snapshot_observed_at":"2026-08-09T18:00:57.783341Z","submitted_at":"2023-02-15T05:36:41Z","title":"Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.07475","snapshot_observed_at":"2026-08-01T09:13:18.033858Z","title":"Sparse-signsgd with majority vote for communication-efficient distributed learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:18.033858Z"},"links":{"cited_paper":"/paper/2302.07475","citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:0d2a8afe4d337c48d0585e8a62179bfacdc62bcc3eb02bfc4b46af63284d98c1","observation_id":"45e179f5-c30c-43ab-8bc8-871310ec5a78","resolution":{"observed_at":"2026-08-01T09:13:18.033858Z","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":"2026-08-01T09:13:18.200539Z","title":"Sign-based gradient descent with heterogeneous data: Convergence and byzantine resilience,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:18.200539Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:8f2ed9a0a11da3d4dfa315d734f8b08069bcf79eac2212d5b1946044c3d2a795","observation_id":"b9c83660-3817-4380-ab41-f6086870abc8","resolution":{"observed_at":"2026-08-01T09:13:18.200539Z","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":"2026-08-01T09:13:18.250279Z","title":"FedLSC: Improving communi- cation efficiency and robustness in federated learning with stragglers and adversaries,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:18.250279Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:c3c35a950460d226cd5f962101db8a20e758670a2f492494c003c2c0c8029615","observation_id":"9234bddf-c254-477b-ba40-3fae1c7dd5f8","resolution":{"observed_at":"2026-08-01T09:13:18.250279Z","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":"2026-08-01T09:13:18.317416Z","title":"Exploiting unintended feature leakage in collaborative learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:18.317416Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:260f7eb28b8e36f96bc38874ff845008499a047b1153d3732d7e084dea0295fa","observation_id":"0de2ea04-8f85-4c7f-839f-e17acd4d7fe9","resolution":{"observed_at":"2026-08-01T09:13:18.317416Z","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":"2026-08-01T09:13:18.464786Z","title":"Deep leakage from gradients,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:18.464786Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:e247a4092e5a9b2c4d0f26d8ff6936499259dc7e610a88b6d4cdd1880286f0fe","observation_id":"9d794930-306c-4d9c-badb-f28f14c9a155","resolution":{"observed_at":"2026-08-01T09:13:18.464786Z","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":"2026-08-01T09:13:18.620127Z","title":"Inverting gradients-how easy is it to break privacy in federated learning?","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:18.620127Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:d0ecd086d1df30ccfa728eafe38032a751c91dccc8acdbb1c4f501b8e39e0980","observation_id":"cda64a94-770c-4eb4-b2a7-5cceb7b0e165","resolution":{"observed_at":"2026-08-01T09:13:18.620127Z","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":"2026-08-01T09:13:18.732916Z","title":"Practical secure aggregation for privacy-preserving machine learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:18.732916Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:75e8bfddcea5e6a36d08218af870c95ac74ecb80bdd0bb09bf68aab7f2c5e2e7","observation_id":"2216eeb4-39f6-4e98-a278-810c9ea32bf0","resolution":{"observed_at":"2026-08-01T09:13:18.732916Z","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":"2026-08-01T09:13:18.852411Z","title":"Secure single-server aggregation with fault tolerance,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:18.852411Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:af0c591be3f3260caad26c35d1c9f52f720006a8624d5baecfc987271b370bcb","observation_id":"96f0b1b6-24eb-487f-9165-d438906c9716","resolution":{"observed_at":"2026-08-01T09:13:18.852411Z","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":"2026-08-01T09:13:19.007964Z","title":"Hi-SAFE: Hierarchical secure aggregation for lightweight federated learning,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:19.007964Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:7604af90bb4c6f95e2dd21959f0fc926f343dd0e8329b312826ac493a1befcdf","observation_id":"4570f58c-98fc-4a82-bc00-629e5ec5b34f","resolution":{"observed_at":"2026-08-01T09:13:19.007964Z","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":"2026-08-01T09:13:19.120666Z","title":"Scalable and unconditionally secure multiparty computation,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:19.120666Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:07cef8b9dd4814ff3d293ad1965be7604d90614abf2741fd6130355127105126","observation_id":"668d6e81-a7a4-4412-9e4c-cbe39f75862d","resolution":{"observed_at":"2026-08-01T09:13:19.120666Z","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":"2026-08-01T09:13:19.264093Z","title":"Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:19.264093Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:ae72db8874f905847d90834e5acf8ea4e17f8d04a4406abfc08a74b459fe5cb1","observation_id":"a494cbac-f9b5-4568-ac47-f065f8012339","resolution":{"observed_at":"2026-08-01T09:13:19.264093Z","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":"2026-08-01T09:13:19.418737Z","title":"A hybrid approach to privacy-preserving federated learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:19.418737Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:f8e93ab4f98a57fc8e40b5e311e4e27019bb4f9de9f610d6bbe347c9091ba696","observation_id":"62c2b80d-bd87-40ca-8c2c-1298ec0fe2a7","resolution":{"observed_at":"2026-08-01T09:13:19.418737Z","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":"2026-08-01T09:13:19.565575Z","title":"Differentially private secure multi- party computation for federated learning in financial applications,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:19.565575Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:ef1d72ea20919fdfaca69bbcc84c72e51f4fae19105a59c4db0d2f08348b7c6e","observation_id":"63945585-567b-4b73-87a5-157a860a25e0","resolution":{"observed_at":"2026-08-01T09:13:19.565575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.04808","last_updated":"2021-05-11T06:43:38Z","snapshot_observed_at":"2026-08-08T00:24:03.555017Z","submitted_at":"2021-05-11T06:43:38Z","title":"DP-SIGNSGD: When Efficiency Meets Privacy and Robustness","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.04808","snapshot_observed_at":"2026-08-01T09:13:19.717387Z","title":"DP-SIGNSGD: When efficiency meets privacy and robustness,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:19.717387Z"},"links":{"cited_paper":"/paper/2105.04808","citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:2ffdda1a9d7a1509d18c16b72139e97ecb5e4878d9e1ca3b157fc5f6e0e16176","observation_id":"1dd4d685-ee22-47d3-954e-c1ee60383cdb","resolution":{"observed_at":"2026-08-01T09:13:19.717387Z","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":"2026-08-01T09:13:19.849186Z","title":"BatchCrypt: Efficient homomorphic encryption for cross-silo federated learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:19.849186Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:0d2844f39fdad0d5f7c70464703141dc5b705424079e898dfff616d1c34a4a7c","observation_id":"b76f0fc0-0c7e-48a4-a1db-8bf4a2c6aafe","resolution":{"observed_at":"2026-08-01T09:13:19.849186Z","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":"2026-08-01T09:13:19.959316Z","title":"Privacy preserving machine learning with ho- momorphic encryption and federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:19.959316Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:12653b398ae07a3fba527233146a070b363bbbb4a921c686c4c26bc57b31219c","observation_id":"b88a41a9-2bf6-4f8e-ad2e-b614a43a53c0","resolution":{"observed_at":"2026-08-01T09:13:19.959316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00675","last_updated":"2021-09-29T07:30:33Z","snapshot_observed_at":"2026-08-12T14:07:07.063179Z","submitted_at":"2021-09-02T02:36:04Z","title":"FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00675","snapshot_observed_at":"2026-08-01T09:13:20.075150Z","title":"FLASHE: Additively symmetric ho- momorphic encryption for cross-silo federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:20.075150Z"},"links":{"cited_paper":"/paper/2109.00675","citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:0f6c5ac0358804a3877655c7ab7c334fb984b50da3dc624351beee559fd2b0f4","observation_id":"f861cf05-f247-4dee-84f3-afc5da22ac51","resolution":{"observed_at":"2026-08-01T09:13:20.075150Z","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":"2026-08-01T09:13:20.208425Z","title":"Privacy-preserving federated learning based on multi-key homomorphic encryption,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:20.208425Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:b40da735150c70df0cc4367e2677a68f240e1288940baf9b3005573198216c30","observation_id":"b94312f8-403b-454c-b907-31e94610e520","resolution":{"observed_at":"2026-08-01T09:13:20.208425Z","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":"2026-08-01T09:13:20.323279Z","title":"Homomorphic encryption for arithmetic of approximate numbers,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:20.323279Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:f3e0515ef2188013d3bba14dc847ee9297f4f598339cdcbfae4fd6b8240260f0","observation_id":"11ec99cc-8e85-431f-8b47-e1fb1c56b2be","resolution":{"observed_at":"2026-08-01T09:13:20.323279Z","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":"2026-08-01T09:13:20.467227Z","title":"Efficient fully homomorphic encryption from (standard) lwe,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:20.467227Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:2e5f9e61229250809f2fa0b85242c5703e64f68c81ae6d64a578df23c08ddf4c","observation_id":"e41391e1-264d-4200-ba7d-3b8da31b8876","resolution":{"observed_at":"2026-08-01T09:13:20.467227Z","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":"2026-08-01T09:13:20.574811Z","title":"Fully homomorphic encryption using ideal lattices,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:20.574811Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:d5b9eb9714c5aa7ce47f86b6d2d4f49e88a9c88b06107534eb17fa6ccbfeffe1","observation_id":"0598796f-8e5c-40ac-aca3-fcf8f37235a4","resolution":{"observed_at":"2026-08-01T09:13:20.574811Z","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":"2026-08-01T09:13:20.690221Z","title":"Efficient multiparty protocols using circuit randomization,","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:20.690221Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:76c8e94b132843db38460c6371e6f9e2875d036924abe3cdfe8c62c707985aa0","observation_id":"8f362f53-3e94-4e4b-a4e6-6092c6a0ccd6","resolution":{"observed_at":"2026-08-01T09:13:20.690221Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.03415","last_updated":"2020-01-04T14:02:45Z","snapshot_observed_at":"2026-08-09T00:50:27.162322Z","submitted_at":"2019-07-08T06:18:02Z","title":"Communication-Efficient (Client-Aided) Secure Two-Party Protocols and Its Application","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.03415","snapshot_observed_at":"2026-08-01T09:13:20.824459Z","title":"Communication-efficient (client-aided) secure two-party protocols and its application,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:20.824459Z"},"links":{"cited_paper":"/paper/1907.03415","citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:6ad300353c2476567703437a8c34488272d859b4fe8198e0831765cb1e96a4ce","observation_id":"7f196e40-096c-4c26-a9c7-0337d123d2cf","resolution":{"observed_at":"2026-08-01T09:13:20.824459Z","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":"2026-08-01T09:13:20.994503Z","title":"ATLAS: efficient and scalable mpc in the honest majority setting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:20.994503Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:1966a58d8fedf6a1df6479ac00c7dc2eac8440dd55bb0ba54e2a9cb40fa67199","observation_id":"49268947-c035-419a-b274-25799b4cd8bf","resolution":{"observed_at":"2026-08-01T09:13:20.994503Z","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":"2026-08-01T09:13:21.102547Z","title":"Applications of fermat’s little theorem in cryptography,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:21.102547Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:42c492e509a0816e27aa6bb36f7e1bd19c74cee1c3c351a859d3f13b04f69a1a","observation_id":"db0e20a6-36e9-4fdd-9e9a-3882ebc88721","resolution":{"observed_at":"2026-08-01T09:13:21.102547Z","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":"2026-08-01T09:13:21.190913Z","title":"Communication-efficient learning of deep networks from decentralized 15 data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:21.190913Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:bd719696f42d931b9105c70c358f08986b889eb581555dbdf3c2198e8017a3f8","observation_id":"6a6a7450-6faf-495c-bc85-c7a119df71d6","resolution":{"observed_at":"2026-08-01T09:13:21.190913Z","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":"2026-08-01T09:13:21.308572Z","title":"Goldreich,Foundations of Cryptography: Volume 2–Basic Applica- tions","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:21.308572Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:8d0d28528a23fd8c4b48a94d939a7b4b468bcd8e431be10e355317a506ac9eac","observation_id":"1ca423a1-9f0c-48b5-99a4-2f0ba5f20aa2","resolution":{"observed_at":"2026-08-01T09:13:21.308572Z","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":"2026-08-01T09:13:21.418110Z","title":"Secure multiparty computation for privacy- preserving data mining,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:21.418110Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:0fbf30f8f982bfd0377063ef369defff0f9f2bc72dafffcd71003fb469b0ce67","observation_id":"a73b68df-387e-414c-a63c-920929fb6fa3","resolution":{"observed_at":"2026-08-01T09:13:21.418110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04937","last_updated":"2023-12-11T04:16:37Z","snapshot_observed_at":"2026-08-07T06:35:42.522149Z","submitted_at":"2023-12-08T10:06:17Z","title":"AHSecAgg and TSKG: Lightweight Secure Aggregation for Federated Learning Without Compromise","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04937","snapshot_observed_at":"2026-08-01T09:13:21.583964Z","title":"AHSecAgg and TSKG: Lightweight secure aggregation for federated learning without compromise,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:21.583964Z"},"links":{"cited_paper":"/paper/2312.04937","citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:51c9ffa0f76d1082708b52fc09b4dd706a36480d5525af030a46b3df05479f2d","observation_id":"e6dc824f-e5e6-4045-88c6-2cefa74eb255","resolution":{"observed_at":"2026-08-01T09:13:21.583964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.10123","last_updated":"2024-05-15T06:40:41Z","snapshot_observed_at":"2026-08-11T16:03:21.558433Z","submitted_at":"2023-03-17T17:03:50Z","title":"Correlations of the Riemann zeta function","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.10123","snapshot_observed_at":"2026-08-01T09:13:21.804076Z","title":"Secure aggregation of semi-honest clients and servers in federated learning with secret-shared homomorphism,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:21.804076Z"},"links":{"cited_paper":"/paper/2303.10123","citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:de31f5a17de2343bfc329952d13f26978971392214b842086c204964f5028cca","observation_id":"71a0a2ae-26d0-4611-8f2a-e2a47559127d","resolution":{"observed_at":"2026-08-01T09:13:21.804076Z","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":"2026-08-01T09:13:21.992942Z","title":"PQSF: Post-quantum secure privacy-preserving federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:21.992942Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:65d41835ca1b6661d183cb2510d68b7b6f79ff1e8d6559c32ecd82c710914a38","observation_id":"d3790479-8439-4b2f-8d94-0a46d073c2fd","resolution":{"observed_at":"2026-08-01T09:13:21.992942Z","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":"2026-08-01T09:13:22.155249Z","title":"Secure and flexible privacy- preserving federated learning based on multi-key fully homomorphic encryption,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:22.155249Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:517cb296d08bb49e66036f0f1468100e80392e5883a7edebbd4a0053dc119ce5","observation_id":"79d7fbbe-1495-49cd-8154-1c34a1324e0f","resolution":{"observed_at":"2026-08-01T09:13:22.155249Z","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":"2026-08-01T09:13:22.334401Z","title":"von zur Gathen and J","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:22.334401Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:6098149ee927ed9c950428e989b5aaa476868b524eaa236edc68f8cb6bdce08d","observation_id":"a40388f4-8166-4250-88c0-da70a51fc87b","resolution":{"observed_at":"2026-08-01T09:13:22.334401Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0912.0352","last_updated":"2009-12-02T08:04:33Z","snapshot_observed_at":"2026-08-07T00:53:10.829634Z","submitted_at":"2009-12-02T08:04:33Z","title":"Enhanced spin injection efficiency in a four-terminal double quantum dot system","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0912.0352","snapshot_observed_at":"2026-08-01T09:13:22.483869Z","title":"Faster polynomial multiplication via multipoint kronecker substitution,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:22.483869Z"},"links":{"cited_paper":"/paper/0912.0352","citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:2b18704f85b4c067d0c45896b1a3bf6207673f70c2fd97f4ec49aa8a8fa073c0","observation_id":"59c4f0fb-9cde-406a-a791-0887a9371620","resolution":{"observed_at":"2026-08-01T09:13:22.483869Z","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":"2026-08-01T09:13:22.670068Z","title":"Gradient-based learning applied to document recognition,","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:22.670068Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:83bc9dfa6958cbab43a38e8c87c10d861e2cf19e2382396fdfcd523b12a520ee","observation_id":"18898c67-28f9-4c90-9909-296bb6c18c0c","resolution":{"observed_at":"2026-08-01T09:13:22.670068Z","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":"2026-08-01T09:13:22.790331Z","title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:22.790331Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:6a54cc1ada334e0271a45fd254c72d78e499f50564007c05476e43356e648ccb","observation_id":"bf5a734a-e3c5-4535-8b1a-4eed8be64330","resolution":{"observed_at":"2026-08-01T09:13:22.790331Z","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":"2026-08-01T09:13:22.918574Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-01T09:13:22.918574Z"},"links":{"citing_paper":"/paper/2607.20890"},"observation_digest":"sha256:5569362613a5478e454300be03b3bbd0aca3ae343ad249537f3a3a8b432e8ce6","observation_id":"3f2126e0-3ef9-433e-a5ab-41f313035ade","resolution":{"observed_at":"2026-08-01T09:13:22.918574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.20890","last_updated":"2026-07-23T03:26:41Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T13:55:14.804736Z","submitted_at":"2026-07-23T03:26:41Z","title":"Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":53,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":53},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2607.20890."}