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Paper Citation Record · LEDGER

Learning to Detect Malicious Clients for Robust Federated Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2002.00211.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2002.00211 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:33:40.068366Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-23T23:13:37.023261Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bd39de33-7b71-448b-9cd6-fa4b644d3007 · inbound

BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning cites this paper.

BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning Learning to Detect Malicious Clients for Robust Federated Learning

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-23T23:13:37.026029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-23T23:09:32.656257Z digest=sha256:c86b0b7d6b40696b11abf68c7dd099fa76c18f59c4300117add02e8c546e86fe

Observation 60fad88a-e755-47fc-8599-b6fc0005eb77 · inbound

Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer cites this paper.

Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer Learning to Detect Malicious Clients for Robust Federated Learning

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-06T05:33:40.068366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:33:40.068366Z digest=sha256:6c6d5c2be06d1c345dfd5715c7637a34ea42415334d44389cb2237327d5c12a4

Observation 4bdafc3c-fe5c-4bc3-9770-019cd40190f2 · inbound

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks cites this paper.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Learning to Detect Malicious Clients for Robust Federated Learning

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-05T16:20:23.334034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:20:23.334034Z digest=sha256:bbb703ca85ce672794da6bd15ff56ffaf2d601277957396150c1ba6ea1c9d380

Observation 7f37acc8-4b67-4fae-8c2c-f64b28fa6bd1 · inbound

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats cites this paper.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Learning to Detect Malicious Clients for Robust Federated Learning

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T13:28:47.141282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:28:47.141282Z digest=sha256:9a40ac4d1bf05c1cb1b1e751f08dd446cdeb0f38fe4d16b2c662b9f99c159836

Observation bb8813a6-ddb6-4e23-979f-87b8378bd672 · inbound

DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning cites this paper.

DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning Learning to Detect Malicious Clients for Robust Federated Learning

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-16T22:38:37.945553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-16T22:33:50.216120Z digest=sha256:de4e5ff3a9a777024c02fbe4747a593a60d0a1fedc99dae21b8e043a7d0e98b7