Paper Citation Record · LEDGER

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors

As of 4 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2606.22700.

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

pith.paper-citation-record.v1
2606.22700 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T10:22:28.671574Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

verified exact2
verified fuzzy0
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation da415bec-ff70-4f8a-b648-b01d2ba9e35d · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Communication-efficient learning of deep networks from decentralized data,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cc24856-0f79-427a-879a-e57cb4d25fb7 · outbound

This paper cites A survey of security threats in federated learning,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors A survey of security threats in federated learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

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Observation 0c1f3eda-ab38-4ec5-b4f2-19e1119c4781 · outbound

This paper cites MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

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Observation bbd2db44-cb9c-4fad-ac93-e019ec3452d8 · outbound

This paper cites FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:09:43.726674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 05fa4399-9047-482f-82d5-642fde1c4bd7 · outbound

This paper cites {FLAME}: Taming backdoors in federated learning,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors {FLAME}: Taming backdoors in federated learning,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

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Observation a54b5498-fe64-45ae-b1da-967d68ca52e8 · outbound

This paper cites Resisting poisoning attacks in federated learning via dual-domain distance and trust assessment,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Resisting poisoning attacks in federated learning via dual-domain distance and trust assessment,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4a8dcc4-d7e5-4983-8418-4e0f375ea8bc · outbound

This paper cites Robust knowl- edge distillation in federated learning: Counteracting backdoor attacks,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Robust knowl- edge distillation in federated learning: Counteracting backdoor attacks,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ddbb54e-c81c-41cc-82ff-37c203ef7ddf · outbound

This paper cites Defending against backdoor attacks in federated learning by using differential privacy and ood data attributes,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Defending against backdoor attacks in federated learning by using differential privacy and ood data attributes,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

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Observation 0bbac3a6-f31e-4baa-b3a1-9c90f26ff259 · outbound

This paper cites Defending the edge: Representative-attention defense against backdoor attacks in federated learning,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Defending the edge: Representative-attention defense against backdoor attacks in federated learning,

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:09:43.723893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 52dc044b-a331-415a-bfdc-c72839f75e04 · outbound

This paper cites Securing federated learning against backdoor threats with foundation model integration,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Securing federated learning against backdoor threats with foundation model integration,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75ed20ce-1984-4227-8683-47a82f3b99f6 · outbound

This paper cites Flpu- rifier: Backdoor defense in federated learning via decoupled contrastive training,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Flpu- rifier: Backdoor defense in federated learning via decoupled contrastive training,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7c790bc-3e35-40b5-a1b0-f6c850e8c96b · outbound

This paper cites Unlearning backdoor attacks in federated learning,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Unlearning backdoor attacks in federated learning,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

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Observation 85b62ec1-e1bc-4d6e-9165-d6f48e74c388 · outbound

This paper cites Fedcleanse: Cleanse the backdoor attacks in federated learning system,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Fedcleanse: Cleanse the backdoor attacks in federated learning system,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10fc4bad-6aae-48d6-b67e-2657a94bf14e · outbound

This paper cites Shift: Enhancing federated learning robustness through client-side backdoor detection,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Shift: Enhancing federated learning robustness through client-side backdoor detection,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8de4f82b-e741-407f-95be-05c239178520 · outbound

This paper cites Adfl: Defending backdoor attacks in federated learning via adversarial distillation,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Adfl: Defending backdoor attacks in federated learning via adversarial distillation,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 586f559c-4443-455f-b4d6-42624b7c7121 · outbound

This paper cites Mitigating distributed backdoor attack in federated learning through mode connec- tivity,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Mitigating distributed backdoor attack in federated learning through mode connec- tivity,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcf23ea2-f25f-4b95-87d4-9c690b8995e2 · outbound

This paper cites Generative adversarial nets,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Generative adversarial nets,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

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Observation 61f83c78-6abe-4a30-b10e-1d8e42e5c212 · outbound

This paper cites Improved training of wasserstein gans,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Improved training of wasserstein gans,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

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Unavailable: canonical work link unavailable.

Observation c6c8ac62-9087-4209-9fab-c6925122788b · outbound

This paper cites Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-07-04T09:09:43.729228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3c355777-bf69-4724-a506-cee1b1fbd4ac · outbound

This paper cites Mudguard: Taming malicious majorities in federated learning using privacy-preserving byzantine-robust clustering,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Mudguard: Taming malicious majorities in federated learning using privacy-preserving byzantine-robust clustering,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

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Observation ddb14572-63d3-4b49-8735-67167163937d · outbound

This paper cites Learning multiple layers of features from tiny images,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Learning multiple layers of features from tiny images,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

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Unavailable: canonical work link unavailable.

Observation 1aa0dbc0-3735-4f5d-85dd-6cfbdd8fac39 · outbound

This paper cites The german traffic sign recognition benchmark: a multi-class classification competition,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors The german traffic sign recognition benchmark: a multi-class classification competition,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

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Unavailable: canonical work link unavailable.

Observation 4b0ebd39-11dc-4b84-8a9c-5e5287841981 · outbound

This paper cites Fine-pruning: Defending against backdooring attacks on deep neural networks,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Fine-pruning: Defending against backdooring attacks on deep neural networks,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

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Unavailable: canonical work link unavailable.

Observation 514e9ee2-e3ef-4572-b3cb-66138a74b9e8 · outbound

This paper cites Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,.

SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-26T10:22:28.671574Z

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Pith citing papers

No inbound Pith citation observations are available.