{"as_of":"2026-08-09T02:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:86233d21dab85a57af9dafdb050de248e4943534a70d661127902a83297936f1","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:42:55.778948Z","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":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.16840/citation-record","integrity":"/paper/2506.16840/integrity","json":"/paper/2506.16840/citation-record.json","paper":"/paper/2506.16840"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:52.414746Z","title":"A tutorial on human activity recognition using body-worn inertial sensors.ACM Computing Surveys (CSUR), 46(3):1–33, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:52.414746Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:08666e754b792ec81411c7bad31a19a46c953613261e5c0fb66cf46340522721","observation_id":"a83c2a37-2da7-4803-b6a5-5138bab24829","resolution":{"observed_at":"2026-08-06T23:42:52.414746Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:43:00.924743Z","title":"The eu general data protection regulation (gdpr).A practical guide, 1st ed., 10(3152676), 2017","venue":null,"work_id":"9dec82e4-5084-42ad-b663-6f8cbc0ff44c","year":2017},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:52.544751Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:23e8ce04d36eb0912dbbacd067cca3585422d2610ece6949e5eb9b91dad6d96e","observation_id":"26c3be5b-e858-4628-bbcb-c114b6fcab84","resolution":{"observed_at":"2026-08-06T23:43:01.044785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:43:00.634424Z","title":"A guide to the california consumer privacy act of 2018.A vailable at SSRN 3275571, 2018","venue":null,"work_id":"7fd24d5e-6e0c-4e21-a0b1-bbce726f0ff6","year":2018},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:52.636357Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:941c450530a5e58d875c41f7b30a2d92c80f5f8a16c33d11a109b7de61730c07","observation_id":"663a874b-ee7e-495a-b8ec-af3842bb36e9","resolution":{"observed_at":"2026-08-06T23:43:00.761322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:52.756914Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:52.756914Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:ec52cf323c1b1263fafd56acd25f8afdc626f6b8edda0c013e44191fefc920d2","observation_id":"3ab72b05-dbd8-44aa-afb5-edb7029e0451","resolution":{"observed_at":"2026-08-06T23:42:52.756914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:43:00.315167Z","title":"Learning from others without sacrificing privacy: Simulation comparing centralized and federated machine learning on mobile health data.JMIR mHealth and uHealth, 9(3):e23728, 2021","venue":null,"work_id":"527495c9-e042-441a-bb43-10e720b51f9a","year":2021},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:52.864806Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:6e7699834df7823b338a1cb1765c3aa201055c8a38906668f78d9fe3ecb91253","observation_id":"f66fb059-8c2e-4ca1-af2b-8fca8738e3f5","resolution":{"observed_at":"2026-08-06T23:43:00.404827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:43:00.085245Z","title":"Evaluation and comparison of federated learning algorithms for human activity recognition on smartphones.Pervasive and Mobile Computing, 87:101714, 2022","venue":null,"work_id":"258126d9-c867-40a0-a675-70bd9d007edb","year":2022},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:53.034946Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:d96b80069ab135a72d8fb068861bb57fdd17301fb0fdf3c864351c952f3b662e","observation_id":"fbfdf8b2-9b62-48fb-b648-6c27aced3954","resolution":{"observed_at":"2026-08-06T23:43:00.221997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:59.881674Z","title":"Fl- pmi: federated learning-based person movement identification through wearable devices in smart healthcare systems.Sensors, 22(4):1377, 2022","venue":null,"work_id":"77337026-3bde-445f-88b4-d8fe2dcbd798","year":2022},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:53.224476Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:f398f93b28a5e748efcded4a188d248d0a4147c5a4eecf91e094d62aabc923e0","observation_id":"ef1d9762-1441-468d-a8cf-872817af45e1","resolution":{"observed_at":"2026-08-06T23:42:59.954757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:59.673991Z","title":"Meta-har: Federated representation learning for human activity recognition","venue":null,"work_id":"a6d81bdc-a682-434b-981b-f863996c16d0","year":2021},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:53.318540Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:ba65708fababb3822670fdaf99380948de7cbdc7a29bb296c100a22e02710157","observation_id":"6f528cac-18c8-4d2c-b3f5-e2f467029946","resolution":{"observed_at":"2026-08-06T23:42:59.776371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:59.264132Z","title":"Protohar: Prototype guided personalized federated learning for human activity recognition.IEEE Journal of Biomedical and Health Informatics, 27(8), 2023","venue":null,"work_id":"0bb0ce4e-8632-4fed-b8a0-7a2c461a0b3b","year":2023},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:53.534752Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:f87a1511e4c5c8542448e2f795d211eb645e962b26f0325a4845a2de3d918b6d","observation_id":"6752ac43-361f-42fb-9e03-a31a24f896d9","resolution":{"observed_at":"2026-08-06T23:42:59.508352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.14390","last_updated":"2022-03-05T20:30:32Z","snapshot_observed_at":"2026-07-06T09:42:35.058716Z","submitted_at":"2020-07-28T17:59:07Z","title":"Flower: A Friendly Federated Learning Research Framework","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.14390","snapshot_observed_at":"2026-08-06T23:42:53.644752Z","title":"Flower: A friendly federated learning research framework.arXiv preprint arXiv:2007.14390, 2020","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:53.644752Z"},"links":{"cited_paper":"/paper/2007.14390","citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:23829d8e2cbb16ac832ef50ca17c2a34212c8a910f2218886f8ef06eadd086e2","observation_id":"0f6ceb44-82e7-4755-976d-ad51dcca0fe0","resolution":{"observed_at":"2026-08-06T23:42:53.644752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:59.075724Z","title":null,"venue":null,"work_id":"8b3d97f8-a4fb-4ccd-9cc4-3e8cd2a1e274","year":2020},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:53.766499Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:ff9e2e6e8616b65a38a7def4d3c9d4306aae9c1dc750e8d0c2e8b31f59811052","observation_id":"0e249788-02ba-47d0-8cef-02f891932c3b","resolution":{"observed_at":"2026-08-06T23:42:59.159129Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:58.773639Z","title":"A federated learning system with enhanced feature extraction for human activity recognition.Knowledge-Based Systems, 229:107338, 2021","venue":null,"work_id":"d4de0f85-ba66-4b37-bfff-033198e824eb","year":2021},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:53.854824Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:438c6907b5b5a17595d6fc4ec8906ffc0edb11d57bc74786399b0d4310db6516","observation_id":"510447e3-0191-40d1-9049-ec4276620cb4","resolution":{"observed_at":"2026-08-06T23:42:58.927338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:58.512489Z","title":"2d federated learning for personalized human activity recognition in cyber- physical-social systems.IEEE Transactions on Network Science and Engineering, 9 (6):3934–3944, 2022","venue":null,"work_id":"24323a3c-0114-4ad7-ab57-894f9171e882","year":2022},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:54.054655Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:58d6601d57af3460e59b4a2c288083e9531cf662e383b891ff53681ecaa28d13","observation_id":"56627a29-e4d0-48a0-8cd6-6c8d60e8f38a","resolution":{"observed_at":"2026-08-06T23:42:58.675964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:58.295157Z","title":null,"venue":null,"work_id":"a6eb860d-15e1-470a-b193-902123658e3a","year":2022},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:54.129326Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:0afe530d7c8a7ecf505a29411c2b43486c705629dd347d0ad3cbb17c64282764","observation_id":"b6600dfc-4d8e-42e6-9199-a8e6194e9f61","resolution":{"observed_at":"2026-08-06T23:42:58.381762Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:54.253851Z","title":"A profile similarity-based personalized federated learning method for wearable sensor-based human activity recognition.Information & Management, 61(7):103922, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:54.253851Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:5b31790d53403897372d1869598ad7777618785ea3cf386240674fdd5145caed","observation_id":"c7198cfc-478c-4adb-8b7a-e3a788f58385","resolution":{"observed_at":"2026-08-06T23:42:54.253851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:58.057634Z","title":null,"venue":null,"work_id":"03d2c6ff-ca9c-4534-8f96-16d63490ad2e","year":2024},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:54.333640Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:0c7e8b72fc68e4f5aa0533b80799233df3c607b484d0ab32a5e8ddc2fe26cb83","observation_id":"0ffe2919-e5b8-4795-bd37-80804bb597a6","resolution":{"observed_at":"2026-08-06T23:42:58.157332Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:57.845634Z","title":"Clusterfl: a similarity-aware federated learning system for human activity recog- nition","venue":null,"work_id":"8754198d-b85b-49e6-bcb4-1ba9a4064fd1","year":2021},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:54.490024Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:562c31ddd3f498c60d7d26e47f1eab003b1a2f6b45f6ee74deec2b63e66e8f5c","observation_id":"4375659e-3cf6-464a-b03b-2ecbd8e6c789","resolution":{"observed_at":"2026-08-06T23:42:57.974412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:57.614753Z","title":"Fedclar: Federated clustering for personalized sensor-based human activity recognition","venue":null,"work_id":"119cad7d-d4c6-4fe6-b028-579e6a129696","year":2022},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:54.634749Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:7c238e29483562b959d9c58c7ab82b0119fa9c947b8ef4a8867dc0ee1b8ab871","observation_id":"41a70cc1-09a8-435c-9f2c-bc04a79c71ba","resolution":{"observed_at":"2026-08-06T23:42:57.754759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:57.407359Z","title":"Feddl: Federated learning via dynamic layer sharing for human activity recognition","venue":null,"work_id":"9c99ac03-5cbc-4c14-9d9f-9794c8200d14","year":2021},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:54.682386Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:ede21acfcbefc9b5ef3d168a0664e038101039bc807cb6b91438f9df49b9ec32","observation_id":"8d3bfb63-08a9-4002-9cc5-376be5fb00cf","resolution":{"observed_at":"2026-08-06T23:42:57.492435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:57.148704Z","title":"Protecting health monitoring privacy in fitness training: A fed- erated learning framework based on personalized differential privacy.Internet Technology Letters, 7(6):e499, 2024","venue":null,"work_id":"1e496528-0fb4-4e39-941c-a232e1103ffe","year":2024},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:54.741997Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:d86e881f55f8fe0eb7fe405fe9b1aa5201526cc8a4552bd16b8d9fbab4267b63","observation_id":"45e295a8-45ce-4466-b097-a63aa8bfc1ec","resolution":{"observed_at":"2026-08-06T23:42:57.318051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:56.951620Z","title":"Flrce: Resource-efficient federated learning with early-stopping strategy.IEEE Trans","venue":null,"work_id":"329c0c3e-70f2-4d8d-b1a6-24f5d99528e1","year":2024},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:54.931908Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:e75ee11a268378df80c64bc8dc821bf9d3ad404b14cdb4727781b590d2c005a2","observation_id":"0a562ee9-dbe8-4545-adb6-47dc18d1419d","resolution":{"observed_at":"2026-08-06T23:42:57.074764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:56.876269Z","title":"Flash: Concept drift adaptation in federated learning","venue":null,"work_id":"13f80372-b732-4f28-81c6-b49b84f25d15","year":2023},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:55.065211Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:e742696fa7f30f00afdb1bf14985049fc1e6b13762e0cd8a3e71615b4f7a935e","observation_id":"ce18f8bd-fc45-4ba9-9df7-637604ef16cc","resolution":{"observed_at":"2026-08-06T23:42:56.937905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:55.214839Z","title":"Early stopping-but when? InNeural Networks: Tricks of the trade, pages 55–69","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:55.214839Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:787a2d9ed065127d683893c1754d0e896e856bedf1635b0f4564c747b09da7dc","observation_id":"e03bfffe-d5ee-457b-a720-d9c3c893111f","resolution":{"observed_at":"2026-08-06T23:42:55.214839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:56.657043Z","title":"Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition.Sensors, 16(1):115, 2016","venue":null,"work_id":"aaed50a1-d167-4326-a7e8-fc8d5f7daa71","year":2016},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:55.277762Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:8bf68bca96e34fbc14744ba2e5e88fa94af1fd785df29d576a9a38a6fcabe855","observation_id":"a299b8ff-61ac-44d7-84de-bb25a86e72fa","resolution":{"observed_at":"2026-08-06T23:42:56.782871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:56.443600Z","title":"Ensembles of deep lstm learners for activity recog- nition using wearables.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 1(2):1–28, 2017","venue":null,"work_id":"459407e2-1f79-46b1-b8a3-0a0b9e2d539c","year":2017},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:55.398530Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:adc262a1fb870931e8527e12f90440b55aa87b275a6b91c48c3f78eae8e63cf4","observation_id":"2766c668-26a0-4e36-b3be-ba70c87038bc","resolution":{"observed_at":"2026-08-06T23:42:56.542504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:56.210189Z","title":"Tinyhar: A lightweight deep learning model designed for human activity recognition","venue":null,"work_id":"87969990-240c-4479-9254-d992b4242fe9","year":2022},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:55.483429Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:f4c34b65dd6317dcf625d62990ad077cdc3b42878cab5dd7eb16c97a64ba7542","observation_id":"e14cc653-a567-47e7-9a37-a5730c944a29","resolution":{"observed_at":"2026-08-06T23:42:56.354743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:56.005151Z","title":null,"venue":null,"work_id":"a69aabd5-7e09-4974-9480-bcfa889e7c85","year":2024},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:55.523300Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:d93a316a1e46aa9b75cc582bf052db0bfab5cd73d5441426654f34b7a5c2cf67","observation_id":"86115aac-a604-40dd-a953-b9e351499d9b","resolution":{"observed_at":"2026-08-06T23:42:56.052893Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.09037","last_updated":"2024-12-12T07:53:17Z","snapshot_observed_at":"2026-07-06T20:05:46.205005Z","submitted_at":"2024-12-12T07:53:17Z","title":"Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.09037","snapshot_observed_at":"2026-08-06T23:42:55.645407Z","title":"Beyond confusion: A fine-grained dialectical ex- amination of human activity recognition benchmark datasets.arXiv:2412.09037, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:55.645407Z"},"links":{"cited_paper":"/paper/2412.09037","citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:734da4f4e232cf2233f07099b9ac49614792887ef00e31d557e4130b9e5b4c0f","observation_id":"5c5da559-4918-48e1-8dea-3458f74f2af9","resolution":{"observed_at":"2026-08-06T23:42:55.645407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:55.894228Z","title":"Demystifying impact of key hyper-parameters in federated learning: A case study on cifar-10 and fashionmnist.IEEE Access, 2024","venue":null,"work_id":"0aff0f15-407b-46b9-a2d9-507798650846","year":2024},"citing_paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:55.778948Z"},"links":{"citing_paper":"/paper/2506.16840"},"observation_digest":"sha256:6d3527bc170a71aec0b44a388bf5a79fb50311812b6564b80827f16052b96976","observation_id":"1f0ddc26-c3ca-4292-9e43-ba96105693de","resolution":{"observed_at":"2026-08-06T23:42:55.950737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.16840","last_updated":"2025-09-12T08:35:02Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T23:35:35.464995Z","submitted_at":"2025-06-20T08:43:39Z","title":"FedFitTech: A Baseline in Federated Learning for Fitness Tracking"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":19},"total_outbound_references":29},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.16840."}