Paper Citation Record · LEDGER

DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2201.00763.

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

pith.paper-citation-record.v1
2201.00763 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:39:53.259427Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:20:07.892662Z

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 74380fa4-5c66-4ca6-81b0-91c8acf4561e · inbound

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning cites this paper.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-09T13:39:53.259427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c69fcd3-9674-4f44-85d3-e4cc5d7a6c72 · inbound

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning cites this paper.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T00:45:23.329331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68627dbf-0303-4baf-8a6b-670526d61580 · inbound

Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement cites this paper.

Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:28:02.254731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6b16f9f2-dfbd-4ec4-921d-16bb4d5af5c9 · inbound

Color Matters: Trigger Color Affects Success in Federated Backdoor Attacks cites this paper.

Color Matters: Trigger Color Affects Success in Federated Backdoor Attacks DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:20:07.894466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.