{"as_of":"2026-08-11T08:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:51ed6b10663e450d61f16c270b4a7766be26b77aa39506e9330f3dbf21b27a7e","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:43:10.439069Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2506.23583/citation-record","integrity":"/paper/2506.23583/integrity","json":"/paper/2506.23583/citation-record.json","paper":"/paper/2506.23583"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:43:17.481025Z","title":"Sok: Differential privacies,","venue":null,"work_id":"2898621a-855f-49cf-83bf-7905e089e9e1","year":2020},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:06.732326Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:c26c35c708643283f0090686cbc68f7a30aabf0e87fb4f0737c8353be3cfb3e4","observation_id":"351cc342-a1d7-4481-aa33-b2490127eef2","resolution":{"observed_at":"2026-08-06T21:43:17.594748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1602.05629","last_updated":"2023-01-26T22:54:08Z","snapshot_observed_at":"2026-08-01T18:04:48.083997Z","submitted_at":"2016-02-17T23:40:56Z","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.05629","snapshot_observed_at":"2026-08-06T21:43:06.812812Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:06.812812Z"},"links":{"cited_paper":"/paper/1602.05629","citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:8e23ac2845e062b50c82bb82d3d6c93bc97abf6b73192b0c572c84d94b634dac","observation_id":"39a42e7e-8b74-4350-8d77-ed35ad9dd4a1","resolution":{"observed_at":"2026-08-06T21:43:06.812812Z","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":"2026-08-06T21:43:17.215667Z","title":"Data poisoning attacks against federated learning systems,","venue":null,"work_id":"a445c877-b8fd-4785-8556-b6223f792718","year":2020},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:06.901740Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:5472377eef1ce18e782b02e22d9866759158d2c20d9f510c35f303c0e398f9b3","observation_id":"17e4354a-c65b-4666-ab26-7b60e721b699","resolution":{"observed_at":"2026-08-06T21:43:17.352115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:16.800400Z","title":"How to backdoor federated learning,","venue":null,"work_id":"44549f79-8ed0-439a-9120-f625e8eb6307","year":2020},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:06.992427Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:460435f2e4ad6aa49d64a313c19bce02cb818ce8c3514120988a97732d619e90","observation_id":"9fa4ad49-65e8-4d08-8a0c-3f6ac8765ac7","resolution":{"observed_at":"2026-08-06T21:43:17.015370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:16.456743Z","title":"An exploratory analysis on users’ contributions in federated learning,","venue":null,"work_id":"d469d327-d29e-487b-9d1f-206447d4d9a5","year":2020},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.047920Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:c1bc48121a120c4a6a0e44d057af926aa9529ef916128cfa8b0cc5848223dc83","observation_id":"2dd64f2b-de08-40ec-a9cf-89c738bc3893","resolution":{"observed_at":"2026-08-06T21:43:16.623090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:16.239248Z","title":"Machine learning with adversaries: Byzantine tolerant gradient descent,","venue":null,"work_id":"e3bc41f2-3563-4e94-a23f-06d881f8f2a7","year":2017},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.167743Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:00acd5a9a502f991787da568898d1ea99ac1c5e0996266a238799f29b5e2f0d7","observation_id":"a0990114-32d1-4ea7-98d9-8dd9bfeae501","resolution":{"observed_at":"2026-08-06T21:43:16.324885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:16.037398Z","title":"A principled approach to data valuation for federated learning,","venue":null,"work_id":"01ece288-4bb9-4c88-a9ef-f807b71332d8","year":2020},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.252909Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:d97ca241384a70fbd0f4460104474e191648736552fb5c0cd8d65e5334c2c54d","observation_id":"645584aa-3f98-4de2-bd85-7838ddf138b3","resolution":{"observed_at":"2026-08-06T21:43:16.130341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:15.893576Z","title":"Fedsv: Byzantine-robust feder- ated learning via shapley value,","venue":null,"work_id":"a6e08ed9-c38a-4315-88e5-731f1f50f996","year":2024},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.343825Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:eec204be89f9ed2ec6aefe944c74e7ebb430577b9785e8d9a1b9b53555b4336a","observation_id":"89d59ff2-5d9c-4ecb-bc14-0e5904b72b42","resolution":{"observed_at":"2026-08-06T21:43:15.967131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:15.663643Z","title":"Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance,","venue":null,"work_id":"293b5ed3-d679-4774-87de-0f5b4e26919f","year":2019},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.430895Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:5a1638917c529a9947b5ccb0902a54c4929e9e085e465f1ac010619f808305b5","observation_id":"b15ed041-7d5b-4818-8e59-08ff05c5de7e","resolution":{"observed_at":"2026-08-06T21:43:15.774277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.05594","last_updated":"2022-05-26T14:41:37Z","snapshot_observed_at":"2026-08-01T16:09:10.918627Z","submitted_at":"2022-02-11T13:25:11Z","title":"The Shapley Value in Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.05594","snapshot_observed_at":"2026-08-06T21:43:07.521517Z","title":"The shapley value in machine learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.521517Z"},"links":{"cited_paper":"/paper/2202.05594","citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:cae5ab9a94f63d2baa0e48513f3d569e12923d4bc9ef67821f11b46cededd05a","observation_id":"c20921f9-af41-4933-b83a-1d42e65231ab","resolution":{"observed_at":"2026-08-06T21:43:07.521517Z","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":"2026-08-06T21:43:15.508914Z","title":"FedGT: Identification of malicious clients in federated learning with secure aggrega- tion,","venue":null,"work_id":"39e2af10-af4f-4e3b-bcd8-b1d6ba2c28b9","year":2025},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.611486Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:fab1a4216499c7131d5b0bc49b4739339dbca7084ad1296a6f32bfcdeca8f97c","observation_id":"ee841680-0167-4670-9a72-25a7825b17fd","resolution":{"observed_at":"2026-08-06T21:43:15.567658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:15.458333Z","title":"Quality inference in federated learning with secure aggregation,","venue":null,"work_id":"815e56b5-5900-492a-8360-1288bcc91079","year":2023},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.698982Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:d124cf8eb53e98b923ff983f9ec314c0a29d45fd901de7b89d5353b0821072e5","observation_id":"45db9a7a-1075-4b56-a88a-a6c4033e505f","resolution":{"observed_at":"2026-08-06T21:43:15.501875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:15.274532Z","title":"A survey on data poisoning attacks and defenses,","venue":null,"work_id":"2700ae9f-5be8-410e-a1c7-fb3a2a34f8f3","year":2022},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.797446Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:cb46a5508f6ade8226d67d07896299d7a7f7ec4b238f41290eec24296dff649c","observation_id":"5d4409e8-50cc-4c17-868d-b1963150084c","resolution":{"observed_at":"2026-08-06T21:43:15.379640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:15.103169Z","title":"Label poisoning is all you need,","venue":null,"work_id":"9b31654c-480a-490b-a42d-fe0c3a374453","year":2023},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.883135Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:95efbca3fc10d922aab0b63fcbd7f77de671fe1390c8048188dc002af50b9fd0","observation_id":"fb8ead44-189d-4829-9bbf-6e3a4a065dba","resolution":{"observed_at":"2026-08-06T21:43:15.184489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:14.887635Z","title":"Autoregressive perturbations for data poisoning,","venue":null,"work_id":"e11c86be-9451-4767-83f0-8f24ee92f648","year":2022},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:07.977959Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:35835ce42fef95086f9f6d5ebcba87ad0ec75396b7387d279a8694103a4fcf31","observation_id":"045c70a6-6958-49a2-a48d-178f553b31bf","resolution":{"observed_at":"2026-08-06T21:43:15.005539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:14.746693Z","title":"Backdoor learning: A survey,","venue":null,"work_id":"a41298fc-9418-4e54-9c20-28e1a6f052b8","year":2022},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:08.042836Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:acf484434ee417f75bdcf9760a975afc6fee1f77f60ff9d656fea8f30685c4fd","observation_id":"00aeabc2-4d2a-47fa-8481-854ae595c0f2","resolution":{"observed_at":"2026-08-06T21:43:14.818171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:14.567726Z","title":"Defense strategies toward model poisoning attacks in federated learning: A survey,","venue":null,"work_id":"451c4e76-8bda-451d-a94b-c7687c01b1dd","year":2022},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:08.149370Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:ac34c3d8ba15fbab0f207dc6e402b4c96bd15acebca6e094bc07b1565532aa72","observation_id":"973ffda7-0cf7-4326-88a3-3841cb0124b2","resolution":{"observed_at":"2026-08-06T21:43:14.623769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:14.374895Z","title":"Learning to invert: Simple adaptive attacks for gradient inversion in federated learning,","venue":null,"work_id":"07676b82-7a8a-4ade-86be-aea9b5733547","year":2023},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:08.249177Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:72a5b8b1f0ac1960d78bada427e6dc4082d6080ca40b345d12316cb74a534e9b","observation_id":"1f094869-9755-42d4-8704-4fb0fc01ee4b","resolution":{"observed_at":"2026-08-06T21:43:14.481924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.04866","last_updated":"2020-07-15T15:35:17Z","snapshot_observed_at":"2026-08-06T06:11:20.847532Z","submitted_at":"2018-08-14T19:20:35Z","title":"Mitigating Sybils in Federated Learning Poisoning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.04866","snapshot_observed_at":"2026-08-06T21:43:08.345131Z","title":"Mitigating sybils in federated learning poisoning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:08.345131Z"},"links":{"cited_paper":"/paper/1808.04866","citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:2633460ddbca36a3f1987d4cd218b7bd7b3d3aa29489aa3cfda2fe8c84e6efff","observation_id":"3361fdc1-32bd-43a6-adef-1f591c57432e","resolution":{"observed_at":"2026-08-06T21:43:08.345131Z","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":"2026-08-06T21:43:14.188104Z","title":"Robust aggregation for fed- erated learning,","venue":null,"work_id":"f58861c0-fdc1-43b4-b49d-4470da3082a2","year":2022},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:08.415678Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:a90b23d7b49686eadf402db1bb7014593e7657275463b2ee0c4bd62d0872898e","observation_id":"b856eec7-1764-4c4c-a586-50349a6bb6ed","resolution":{"observed_at":"2026-08-06T21:43:14.272492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:14.006825Z","title":"Byzantine-resilient secure federated learning,","venue":null,"work_id":"b1c94f0d-9084-4ec5-ad27-88d460c1b2a6","year":2020},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:08.490901Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:7567b8b97e8f1ef582026873a0e6132179b4e76903e9a8587419cfe1b661889d","observation_id":"05f4cc04-20cd-4916-8d4a-fc2f8d04032a","resolution":{"observed_at":"2026-08-06T21:43:14.099115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:13.859437Z","title":"Advances and open problems in federated learning,","venue":null,"work_id":"874eda13-482a-435b-90d0-406d578cedc7","year":2021},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:08.553717Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:2cb57a9db120b102a2ba88358d6a47783ef10b2aa96dacad4b484a08808895f9","observation_id":"7f07a7b5-6eb6-4613-a313-94b5c4e031d2","resolution":{"observed_at":"2026-08-06T21:43:13.934075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:13.704780Z","title":"Fast-convergent federated learning with adaptive weighting,","venue":null,"work_id":"9dc239ac-1c42-4ec8-966f-62f8c50e5c7e","year":2021},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:08.662889Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:5622653008047b8f671ff10af36daabda095bccc3b2aa79943e30da86a19b6ff","observation_id":"d398a8dc-fa07-4be2-8d89-0704ce0825c7","resolution":{"observed_at":"2026-08-06T21:43:13.749050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:13.521499Z","title":"Byzantine- robust federated learning via cosine similarity aggregation,","venue":null,"work_id":"2ce3b6cd-7f4f-47ad-96a2-ee36b38a8597","year":2024},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:08.745071Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:63f7a9981ce0beb7c40d0ddec4ff77c77bd99b40a712a97a50fdecd6e5535a3c","observation_id":"07390fcf-f8f5-4c4f-8093-5730abae6174","resolution":{"observed_at":"2026-08-06T21:43:13.606132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:13.391676Z","title":"A survey on homomor- phic encryption schemes: Theory and implementation,","venue":null,"work_id":"c136fb5b-f7b8-4044-a342-109e4a28a7c9","year":2018},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:08.847359Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:fad5f6806731741b032ea234c4d06039b80da0f0d6db747a91df68206352457b","observation_id":"b4ab372d-7914-4f8a-89e3-c3c4a25752f0","resolution":{"observed_at":"2026-08-06T21:43:13.454966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:13.228419Z","title":"Xai—explainable ai,","venue":null,"work_id":"a11d07a0-e59c-4129-8b34-95b8086893ab","year":2019},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:09.002675Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:903a46b811fc228585a7c3ca9b712de488f232234c5b3826e26f1ea09614d7f1","observation_id":"59e54144-4fd9-4a72-abe1-6c9d8eb32068","resolution":{"observed_at":"2026-08-06T21:43:13.314293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:13.031790Z","title":"Beyond accuracy: What data quality means to data consumers,","venue":null,"work_id":"c48125db-7455-490a-be5e-c483ff3b39d0","year":1996},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:09.127019Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:256bf5b03240dd12cb4ac90554b0ffa06defccc4840ff111f0bff0ec115ca4c0","observation_id":"9a49d42a-663a-429f-8a80-9f0628804786","resolution":{"observed_at":"2026-08-06T21:43:13.136285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:12.839320Z","title":"Measure contribution of participants in federated learning,","venue":null,"work_id":"ee25c430-b6a8-4a3f-a09d-46eaebd4b655","year":2019},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:09.249799Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:ac870ded3701a5c9e961f6dccc01d3e6fead576f02bc8662fabc9a9ea4be8fc5","observation_id":"fb336b6c-abc1-4856-9092-da29a9e19d3b","resolution":{"observed_at":"2026-08-06T21:43:12.932529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:12.679186Z","title":"Gradient driven rewards to guarantee fairness in collaborative machine learning,","venue":null,"work_id":"29dd4dbf-2395-48f5-8507-73f52c37a64a","year":2021},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:09.313653Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:10f3f3cab768f93577be19051a846fe0cec6524582929d59f626ad8cd668b6f9","observation_id":"7ef2adf3-c42c-4d65-81ec-436f1257feaf","resolution":{"observed_at":"2026-08-06T21:43:12.747413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:12.498021Z","title":"The shapley value,","venue":null,"work_id":"af4b5369-2f67-48c6-be21-1669aa18f8c7","year":2002},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:09.478315Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:c2eae42e7df34f3725b6fa1e42ede8ab1f70742e08a4948aeca6b82af1f6494f","observation_id":"6e5dafda-e8af-4f2b-9cdf-6fd9fa1a81d6","resolution":{"observed_at":"2026-08-06T21:43:12.600721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.04856","last_updated":"2024-12-25T10:01:04Z","snapshot_observed_at":"2026-07-06T13:50:57.502239Z","submitted_at":"2022-09-11T13:10:29Z","title":"Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)","version":5},"cited_work":{"arxiv_id":"2209.04856","doi":null,"metadata_source":"pith","pith_arxiv_id":"2209.04856","snapshot_observed_at":"2026-08-06T21:43:10.753514Z","title":"Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)","venue":"cs.CR","work_id":"3e710ff6-45ee-43fd-a043-96dbbe92db21","year":2022},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:09.594461Z"},"links":{"cited_paper":"/paper/2209.04856","citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:167a7e2f4412055ae3b7d5e950550af64f1f40417fba240152b677a50077cb65","observation_id":"62559ea2-4177-4bda-8062-224c2db39678","resolution":{"observed_at":"2026-08-06T21:43:10.833900Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.00511","last_updated":"2022-06-01T14:14:24Z","snapshot_observed_at":"2026-08-10T20:35:44.321170Z","submitted_at":"2022-06-01T14:14:24Z","title":"Differentially Private Shapley Values for Data Evaluation","version":1},"cited_work":{"arxiv_id":"2206.00511","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.00511","snapshot_observed_at":"2026-08-06T21:43:10.577457Z","title":"Differentially Private Shapley Values for Data Evaluation","venue":"cs.LG","work_id":"0b8dec52-5af7-4998-98a5-1dfb26d811f9","year":2022},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:09.666618Z"},"links":{"cited_paper":"/paper/2206.00511","citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:0f3131e96c4180af5f1ec78779f1a40d3f1136d7db1f286d0be843dbf51f63aa","observation_id":"a5fef66e-bd7b-455c-b20e-92779b59c674","resolution":{"observed_at":"2026-08-06T21:43:10.658861Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:12.305649Z","title":"Transparent contribution evaluation for secure federated learning on blockchain,","venue":null,"work_id":"a81ec69a-ec9c-4a31-ba37-14cf7339ad7d","year":2021},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:09.749980Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:32f0b9123ea8651f188844f260ee9793ab79d2ee6b617ce29f72270b798f2ae6","observation_id":"329ad383-9c95-450c-b7ab-790949aa0636","resolution":{"observed_at":"2026-08-06T21:43:12.398660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:12.125313Z","title":"Measuring contributions in privacy- preserving federated learning,","venue":null,"work_id":"31225892-9d25-48f6-ac63-e016b10dfe0e","year":null},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:09.852826Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:4662f5c849ebfaaae53c1f886a7f40b893b657a39a0d1b54bbab5df2c0ac8daf","observation_id":"7c20b9da-7786-49ec-9e49-1c8669c9bc39","resolution":{"observed_at":"2026-08-06T21:43:12.209943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:11.949219Z","title":"Inferring contributions in privacy-preserving fed- erated learning,","venue":null,"work_id":"8c3a4a84-de90-4aa1-abd2-5da3e072b3b2","year":2025},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:09.964337Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:a3fb9b6c1c5daeebd4c9124697e541e7797305bc2e2388b501ff6168f4d6683e","observation_id":"369ccf67-1c4d-4c0a-a8fd-b4e8652e37ca","resolution":{"observed_at":"2026-08-06T21:43:12.027824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:11.733997Z","title":"Leave one out error, stability, and generalization of voting combinations of classifiers,","venue":null,"work_id":"5c1e9ed0-f83a-4c02-99de-ea246b778aef","year":2004},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:10.047902Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:5815c3670b8bd27f90366968e36b5a4bde31bbd9e1ca9cc9d5b5c3634da948eb","observation_id":"f98a370d-7b19-4e74-a4a5-b85d24b203bd","resolution":{"observed_at":"2026-08-06T21:43:11.848129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:11.517051Z","title":"Leave-one-out unfairness,","venue":null,"work_id":"fc526e77-36b3-4af4-8d9a-58bbcd6bd4ec","year":2021},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:10.133944Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:3cb519ed0974d26e36e43700bb9476970fe578c0fb6eec50e9b451c77fdbd258","observation_id":"f0a19447-a360-44b9-96a8-ee9bfcccf4e3","resolution":{"observed_at":"2026-08-06T21:43:11.607521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:11.307034Z","title":"Flamby: datasets and benchmarks for cross-silo federated learning in realistic healthcare settings,","venue":null,"work_id":"8c64d3b5-7414-417e-80f7-bcc2440e2c7b","year":2022},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:10.234850Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:640de41d24232db809e94b1a7951cec8ae8caaade3ffa19e6b65f1c0ffb6db9c","observation_id":"bc6b5147-8e08-41b4-b574-0710e1bd5d50","resolution":{"observed_at":"2026-08-06T21:43:11.422296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:11.164854Z","title":"Shapleyfl: Robust federated learning based on shapley value,","venue":null,"work_id":"0d1721a3-e2cb-4497-bb15-66c6b48d8789","year":2023},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:10.338469Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:8967dda03ab6e56f4dce2df95f7c88ad36f403f2009498b62c72e37059b31e47","observation_id":"ecd3daf5-91b3-4d77-8929-85c94f3b14c3","resolution":{"observed_at":"2026-08-06T21:43:11.234583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T21:43:10.971772Z","title":"Opendataval: a unified benchmark for data valuation,","venue":null,"work_id":"bd41033d-105a-4c9f-9d95-e8b89c7cece0","year":2023},"citing_paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:10.439069Z"},"links":{"citing_paper":"/paper/2506.23583"},"observation_digest":"sha256:add518234b5e3c82708e06d753224b7226613eb44280026571691305af48e765","observation_id":"a791e434-9ac8-45cd-8e28-d46b3a511374","resolution":{"observed_at":"2026-08-06T21:43:11.077364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.23583","last_updated":"2025-06-30T07:40:18Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-09T01:52:09.329902Z","submitted_at":"2025-06-30T07:40:18Z","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":2,"verified_fuzzy":35},"total_outbound_references":40},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.23583."}