{"as_of":"2026-08-09T13:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d2149cead35c9f3e6e14f71029f63127753cbb916303660b97eb4f4f04393533","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T20:59:38.220609Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T00:47:11.112524Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-06-29T00:52:55.685113Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"cited_work":{"arxiv_id":"2602.21949","doi":null,"metadata_source":"pith","pith_arxiv_id":"2602.21949","snapshot_observed_at":"2026-06-29T00:52:55.685113Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","venue":"cs.DC","work_id":"71baa356-ff10-4464-8e0c-8a6543938fc8","year":2026},"citing_paper":{"arxiv_id":"2605.29939","last_updated":"2026-05-28T13:51:28Z","snapshot_observed_at":"2026-08-09T00:02:20.427185Z","submitted_at":"2026-05-28T13:51:28Z","title":"CRB-Guided Framework Design and Resource Allocation for Indoor mmWave ISCC Systems","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T00:47:11.112524Z"},"links":{"cited_paper":"/paper/2602.21949","citing_paper":"/paper/2605.29939"},"observation_digest":"sha256:32d88c3f5cea2f21ab3ac0d07d6d05975ef2df96d5b905673b8788ff07c247bc","observation_id":"bf538db3-218e-4249-943d-37823eaa1fb9","resolution":{"observed_at":"2026-06-29T00:52:55.686526Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2602.21949/citation-record","integrity":"/paper/2602.21949/integrity","json":"/paper/2602.21949/citation-record.json","paper":"/paper/2602.21949"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T20:59:36.160284Z","title":"Energy-eﬀicient feder- ated edge learning with streaming data: A lyapunov optimiza- tion approach,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.160284Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:e5215f90a1ac5ed22b43a53091a4694851d20df59963aefec5241f1ea8393ba4","observation_id":"b8f33722-c241-4f01-a392-a74199387b20","resolution":{"observed_at":"2026-08-02T20:59:36.160284Z","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":"2026-08-02T20:59:36.216178Z","title":"Multi-stage hybrid federated learning over large-scale d2d-enabled fog networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.216178Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:e7621a9c986e3a6d2cb4a7b9be7b36fda126fedaeb87b2c15ff6a6d8b50b24ea","observation_id":"0572acee-6173-41b5-b6c1-26fd54c1bfe8","resolution":{"observed_at":"2026-08-02T20:59:36.216178Z","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":"2026-08-02T20:59:36.269694Z","title":"Federated edge network utility maximization for a multi-server system: Algorithm and convergence,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.269694Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:faa5527797614f165f49bae8652584eb4ef20ecfd78ef2f7f0e0dfb25b2a169d","observation_id":"719e22b0-2edd-4c5c-a8ce-a3a98ff441c3","resolution":{"observed_at":"2026-08-02T20:59:36.269694Z","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":"2026-08-02T20:59:36.334223Z","title":"Distributed machine learning for uav swarms: Comput- ing, sensing, and semantics,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.334223Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:501a9e63e24250df42a93a6814d35334eecd798cded84e234030c8eb396b444e","observation_id":"c6b5cda2-ca53-454a-9c06-49a4bc6dcacd","resolution":{"observed_at":"2026-08-02T20:59:36.334223Z","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":"2026-08-02T20:59:36.446538Z","title":"Joint layer selection and differential privacy design for feder- ated learning over wireless networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.446538Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:8ef30a21639818afd12d5d43c9eaff868d621918eb821731b8402d647f929088","observation_id":"3574dcb7-8538-4735-9b61-460beaeafdf5","resolution":{"observed_at":"2026-08-02T20:59:36.446538Z","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":"2026-08-02T20:59:36.508899Z","title":"Prive-hd: Privacy- preserved hyperdimensional computing,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.508899Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:a5c5ced7e37db8b8742cbd727bb94bd079172d3d77d0e7079de80ed5614d682d","observation_id":"f48abed0-b426-4620-a899-d95939585695","resolution":{"observed_at":"2026-08-02T20:59:36.508899Z","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":"2026-08-02T20:59:36.634021Z","title":"Energy eﬀicient federated learning over wireless communica- tion networks,","venue":null,"work_id":null,"year":1935},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.634021Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:cdca7348f7a17c6f387127a78b79077c4e4e61c61a31b8ed5091a203bec54bae","observation_id":"f4e928f1-0031-473b-b493-3c0991cad504","resolution":{"observed_at":"2026-08-02T20:59:36.634021Z","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":"2026-08-02T20:59:36.721930Z","title":"Energy eﬀicient federated learning over heterogeneous mobile devices via joint design of weight quantization and wireless transmis- sion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.721930Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:1f449c372977ba704b8193f7da7bac2c0d633d80c04f3431d4fa06ec8cc0d992","observation_id":"39d30c58-d553-47ad-8540-96762e4d4900","resolution":{"observed_at":"2026-08-02T20:59:36.721930Z","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":"2026-08-02T20:59:36.771383Z","title":"Fl-hdc: Hyper- dimensional computing design for the application of federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.771383Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:81eeba4d37c53fb6eab646309fdcddbae12a533fd0e2db149562400c6c7b8695","observation_id":"aea4e1be-3550-4a44-a67e-225347a51aa0","resolution":{"observed_at":"2026-08-02T20:59:36.771383Z","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":"2026-08-02T20:59:36.829037Z","title":"HyperFeel: An eﬀicient federated learning framework using hyperdimensional comput- ing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.829037Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:d743094b64da05efd491c29d05fc7729e18de93f963e68b5b641e2b8f2b91cf0","observation_id":"00abba93-0aaf-4ba8-ba0c-9cc631aa515f","resolution":{"observed_at":"2026-08-02T20:59:36.829037Z","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":"2026-08-02T20:59:36.911021Z","title":"Hyperdimensional computing empowered federated foundation model over wireless networks for meta- verse,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.911021Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:c4ea32d474fcea0d88b40ed56dbee470b17d74b8caf2d8f2825fee9f7c3c9dcc","observation_id":"e4afc4f7-384b-4901-a2ed-ad54da43a76b","resolution":{"observed_at":"2026-08-02T20:59:36.911021Z","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":"2026-08-02T20:59:36.958010Z","title":"Private and eﬀicient learning with hyperdimensional computing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:36.958010Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:e9cafa896c5ca5896178cdf1dd55c16e7ed145039e503717480e2330212165a9","observation_id":"f3bb9cd4-5fc4-42f6-94f7-81463503a653","resolution":{"observed_at":"2026-08-02T20:59:36.958010Z","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":"2026-08-02T20:59:37.130604Z","title":"Privacy- preserving federated learning with differentially private hyperdi- mensional computing,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.130604Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:0461748fcb3cccef233b98571ac022684a82352353e0a971df98170bdd7c5ee5","observation_id":"5653ff20-7ec2-49b6-aee6-ba77901ad74e","resolution":{"observed_at":"2026-08-02T20:59:37.130604Z","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":"2026-08-02T20:59:37.310318Z","title":"Hydrea: Utilizing hyperdimensional computing for a more robust and eﬀicient machine learning system,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.310318Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:1ea80c25dc0ce82bb848a87ebcbafde9626414fff753e7cb0bb3a755630b38d9","observation_id":"cffe065d-3d9d-4556-a906-27f770a4de75","resolution":{"observed_at":"2026-08-02T20:59:37.310318Z","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":"2026-08-02T20:59:37.444803Z","title":"A highly energy-eﬀicient hyperdimensional computing processor for biosignal classification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.444803Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:8598c4682c223044579fc6735e27c409003a84e73f75a3661452254a74abc407","observation_id":"14b7affb-8015-44d9-85f4-2151146105d4","resolution":{"observed_at":"2026-08-02T20:59:37.444803Z","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":"2026-08-02T20:59:37.518101Z","title":"On hyperdi- mensional computing-based federated learning: A case study,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.518101Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:ace05bbcb66c7862efca08189fb9183c4f6a91c43494aaa64797d0823e465af3","observation_id":"21b48416-4df0-447f-b705-b121a68e0109","resolution":{"observed_at":"2026-08-02T20:59:37.518101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18432","last_updated":"2025-06-24T06:26:34Z","snapshot_observed_at":"2026-08-09T00:01:51.589063Z","submitted_at":"2025-06-23T09:13:54Z","title":"A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.18432","snapshot_observed_at":"2026-08-02T20:59:37.582565Z","title":"A new pathway to integrated learning and commu- nication (ilac): Large ai model and hyperdimensional computing for communication,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.582565Z"},"links":{"cited_paper":"/paper/2506.18432","citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:026d4796eaecc47267064cd36fd1c9419c5d368298959abe973ac007fb5218c5","observation_id":"e9564013-55ec-429a-87ef-d8e6656f5ea4","resolution":{"observed_at":"2026-08-02T20:59:37.582565Z","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":"2026-08-02T20:59:37.658493Z","title":"Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.658493Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:a1c37188e3e45e321c8a09b76b9ad6a66ae68acec57e5bcf53e18acde0095948","observation_id":"641a833b-dbe6-4f16-8d63-e0ad9dc497b4","resolution":{"observed_at":"2026-08-02T20:59:37.658493Z","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":"2026-08-02T20:59:37.726759Z","title":"Classification using hyperdimensional computing: A review,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.726759Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:86cc444b7f09040eefb53d1e75bac291fb1d693644e834734494116223d02478","observation_id":"18dd6fcc-4bbf-4e5b-8fca-b11333aa12cb","resolution":{"observed_at":"2026-08-02T20:59:37.726759Z","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":"2026-08-02T20:59:37.803458Z","title":"Hyperdimensional computing vs. neural networks: Comparing architecture and learning process,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.803458Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:5d97257515b44a4a21f219b12180b0660b622b6a35da33581fb9d3bcfef26f4e","observation_id":"562f85a8-e6c4-4356-8068-4cb52c1096a3","resolution":{"observed_at":"2026-08-02T20:59:37.803458Z","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":"2026-08-02T20:59:37.854294Z","title":"Hyperdimensional biosignal processing: A case study for EMG- based hand gesture recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.854294Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:7cf639933c72b0c8ff5d08a3042c4db6a87a78bc54efeed2b28165dbb186b262","observation_id":"35d5f61a-f6d7-4538-b544-92804f4f4bc0","resolution":{"observed_at":"2026-08-02T20:59:37.854294Z","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":"2026-08-02T20:59:37.904238Z","title":"Calibrating noise to sensitivity in private data analysis,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.904238Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:019326e1e65c88b5b314c743da297134e53d74f3a162bdb7d0d5c1ab0580567f","observation_id":"168ad6eb-6fc7-4c2c-8458-c1b6e72582fc","resolution":{"observed_at":"2026-08-02T20:59:37.904238Z","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":"2026-08-02T20:59:37.974431Z","title":"Concentrated differential privacy: Simplifications, extensions, and lower bounds,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:37.974431Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:9e030d06fa59262f059be74c3781fc49b20e32825e381216a716b75651f8ea58","observation_id":"40956094-4879-406b-bd37-ea6a53c0900d","resolution":{"observed_at":"2026-08-02T20:59:37.974431Z","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":"2026-08-02T20:59:38.007741Z","title":"Private and eﬀicient learning with hyperdimensional computing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:38.007741Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:76e5513669062fa57349a3fdc0197dde902be765274cd015cdd66312012c1399","observation_id":"b7fb6e9e-d888-4acc-b57f-ea3b93d5d0e5","resolution":{"observed_at":"2026-08-02T20:59:38.007741Z","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":"2026-08-02T20:59:38.103193Z","title":"Energy-eﬀicient resource allocation for mobile-edge computation offloading,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:38.103193Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:faa615348105884e64e02b8ecddb14f579e9fd686e1c9994963d7fcc4dc3c6e6","observation_id":"2970d90c-31e5-425b-8d5d-ac85654fad2c","resolution":{"observed_at":"2026-08-02T20:59:38.103193Z","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":"2026-08-02T20:59:38.144752Z","title":"Dynamic computation offloading for mobile-edge computing with energy harvesting devices,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:38.144752Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:7b6a2503ef6b83190562e9ea1ebfc22f73aa306cfc54d30c747e335a9ab95b7f","observation_id":"38aafec1-f849-4b5e-aa11-4c81e31ca641","resolution":{"observed_at":"2026-08-02T20:59:38.144752Z","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":"2026-08-02T20:59:38.212428Z","title":"A tutorial on decomposition methods for network utility maximization,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:38.212428Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:4fe1b034a79911134c474b551add074791c9147404eedfe68c019a3a0cefd8d1","observation_id":"b565f724-2983-46c5-88e7-41ba0679949e","resolution":{"observed_at":"2026-08-02T20:59:38.212428Z","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":"2026-08-02T20:59:38.220609Z","title":"Communication-eﬀicient learning of deep networks from decentralized data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-02T20:59:38.220609Z"},"links":{"citing_paper":"/paper/2602.21949"},"observation_digest":"sha256:f596c70a5cfa4e1d6a2cfe09b52cbfdf284cf23990b14afd56290f120222d71f","observation_id":"919bb811-8f04-42d2-8f49-36ed2649204c","resolution":{"observed_at":"2026-08-02T20:59:38.220609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2602.21949","last_updated":"2026-06-08T16:45:17Z","latest_version":2,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-09T00:01:48.852193Z","submitted_at":"2026-02-25T14:33:17Z","title":"Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":28,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":28},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2602.21949."}