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Paper Citation Record · LEDGER

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning

As of 7 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2507.00423.

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

pith.paper-citation-record.v1
2507.00423 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:23:41.069355Z

measured 58 of 58 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 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

58 of 58 outbound references displayed

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  • verified fuzzy47
  • unresolved10
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b1cb6a79-3aae-4fcc-a4b1-cfd50a5ecdec · outbound

This paper cites an unresolved cited work.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Unresolved cited work

Reference 1

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Source-reported events for the cited work

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Observation 96ba4f03-96c2-4535-b186-87f63874cd2e · outbound

This paper cites Sparse Communication for Distributed Gradient Descent.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Sparse Communication for Distributed Gradient Descent

Reference 2

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Source-reported events for the cited work

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Observation 19a16598-578f-4af3-9bd0-5ff5cefd0612 · outbound

This paper cites How to backdoor federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning How to backdoor federated learning

Reference 3

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Source-reported events for the cited work

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Observation b319cac7-4bb0-4d78-acf1-6c0fcb26e26f · outbound

This paper cites A little is enough: Circumventing defenses for distributed learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning A little is enough: Circumventing defenses for distributed learning

Reference 4

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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.

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Observation 7c4cbb3d-1fec-45e2-a085-00155eab1909 · outbound

This paper cites Analyzing federated learning through an adversarial lens.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Analyzing federated learning through an adversarial lens

Reference 5

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Source-reported events for the cited work

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Observation 64ea39b6-a431-441f-98bd-ec84f7d5d36d · outbound

This paper cites Poisoning attacks against support vector machines.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Poisoning attacks against support vector machines

Reference 6

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Source-reported events for the cited work

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Observation 1c84e5d6-8156-425e-bddd-8022fe2d64f3 · outbound

This paper cites Machine learning with adversaries: Byzantine tolerant gradient descent.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Machine learning with adversaries: Byzantine tolerant gradient descent

Reference 7

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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.

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Observation 087293d8-50f8-4615-852e-f3cf8742270b · outbound

This paper cites Fltrust: Byzantine-robust federated learning via trust bootstrapping.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Fltrust: Byzantine-robust federated learning via trust bootstrapping

Reference 8

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Source-reported events for the cited work

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Observation cfe68e1d-c210-4156-b4b0-cefa9f679323 · outbound

This paper cites Membership inference attacks from first principles.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Membership inference attacks from first principles

Reference 9

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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.

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Observation deca3a9f-6a53-4bc3-bf61-deddd60f842e · outbound

This paper cites Gan- leaks: A taxonomy of membership inference attacks against generative models.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Gan- leaks: A taxonomy of membership inference attacks against generative models

Reference 10

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verified fuzzy
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Source-reported events for the cited work

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Observation 8b7e60c2-0712-462b-9388-cd4fc921b09d · outbound

This paper cites Label-only membership in- ference attacks.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Label-only membership in- ference attacks

Reference 11

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Source-reported events for the cited work

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Observation e761cd8d-7d44-4916-b8d6-1bbd007babdf · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning An analysis of single-layer networks in unsupervised feature learning

Reference 12

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Source-reported events for the cited work

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Observation 88c1a28b-2de6-4482-8dd2-30d26bd8cc8f · outbound

This paper cites Differential privacy: A survey of results.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Differential privacy: A survey of results

Reference 13

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Source-reported events for the cited work

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Observation b0082503-c812-4a49-a74b-8ad3c94922f9 · outbound

This paper cites Local model poisoning attacks to byzantine-robust federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Local model poisoning attacks to byzantine-robust federated learning

Reference 14

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verified fuzzy
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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.

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Observation 41f82b5a-4034-4d89-b68c-084cea7488a4 · outbound

This paper cites Aflguard: Byzantine-robust asynchronous federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Aflguard: Byzantine-robust asynchronous federated learning

Reference 15

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verified fuzzy
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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.

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Observation f9f11340-3d07-43d0-a0da-52fce78c41c5 · outbound

This paper cites Byzantine- robust decentralized federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Byzantine- robust decentralized federated learning

Reference 16

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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.

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Observation 7c95d37f-4606-4338-9e00-eb980b2f2714 · outbound

This paper cites Byzantine-robust federated learning over ring-all-reduce dis- tributed computing.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Byzantine-robust federated learning over ring-all-reduce dis- tributed computing

Reference 17

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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.

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Observation d769cc9c-9649-4d2b-854a-191aac23f172 · outbound

This paper cites Do we really need to design new byzantine-robust aggregation rules? In NDSS, 2025.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Do we really need to design new byzantine-robust aggregation rules? In NDSS, 2025

Reference 18

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Source-reported events for the cited work

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Observation 4d700dd1-b15c-421e-b0c5-6fa353d74635 · outbound

This paper cites Provably robust federated reinforcement learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Provably robust federated reinforcement learning

Reference 19

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Source-reported events for the cited work

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Observation 2802a161-ae71-4818-8a88-11dc123c372d · outbound

This paper cites Active inference against federated learning: Attacks and solutions.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Active inference against federated learning: Attacks and solutions

Reference 20

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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.

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Observation 9d017d88-8d32-468b-899e-335fa9064416 · outbound

This paper cites Chal- lenges in representation learning: A report on three machine learning contests.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Chal- lenges in representation learning: A report on three machine learning contests

Reference 21

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Source-reported events for the cited work

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Observation f879ed53-9346-432d-acf6-ab289d7f0de9 · outbound

This paper cites Membership inference at- tacks on machine learning: A survey.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Membership inference at- tacks on machine learning: A survey

Reference 22

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Source-reported events for the cited work

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Observation 1ab2bb9a-0b7d-458d-8245-623b65464386 · outbound

This paper cites Advances and open problems in federated learn- ing.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Advances and open problems in federated learn- ing

Reference 23

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Source-reported events for the cited work

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Observation 85839bfa-ccb3-40c9-87d2-1788c97dc126 · outbound

This paper cites Maximizing the spread of influence through a social network.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Maximizing the spread of influence through a social network

Reference 24

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Source-reported events for the cited work

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Observation faabbf84-1f44-445a-8e2b-2675ab3df4d2 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Adam: A Method for Stochastic Optimization

Reference 25

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Source-reported events for the cited work

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Observation 382ffa56-80e7-42f8-9dbe-82f6e60b5125 · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Federated Learning: Strategies for Improving Communication Efficiency

Reference 26

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f4c6c4bf-114a-4969-b480-7ee0929e1f39 · outbound

This paper cites Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Reference 27

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unresolved
no resolver link, observed 2026-08-06T21:23:37.230172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d9977661-4565-40a8-99be-c5a050d954cb · outbound

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

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Learning multiple layers of features from tiny images

Reference 28

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Source-reported events for the cited work

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Observation 653baaac-5997-4290-9256-a7b3c1a63b2d · outbound

This paper cites Stolen memories: Leverag- ing model memorization for calibrated white-box member- ship inference.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Stolen memories: Leverag- ing model memorization for calibrated white-box member- ship inference

Reference 29

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verified fuzzy
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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.

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Observation 2a7c0316-7bf5-4da9-8528-19e58a5c8bb5 · outbound

This paper cites Learning to at- tack federated learning: A model-based reinforcement learn- ing attack framework.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Learning to at- tack federated learning: A model-based reinforcement learn- ing attack framework

Reference 30

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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.

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Observation b0aeb968-db5f-4f8a-ac57-84948f658634 · outbound

This paper cites Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare

Reference 31

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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.

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Observation 7bcc8632-c21b-47a2-af6b-2e06958e5476 · outbound

This paper cites Loden: Making every client in federated learning a defender against the poisoning membership infer- ence attacks.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Loden: Making every client in federated learning a defender against the poisoning membership infer- ence attacks

Reference 32

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raw_fallback, observed 2026-08-06T21:23:47.633489Z

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.

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Observation beffa024-e20a-4dc3-88b8-391cd06553fd · outbound

This paper cites Prop- erty inference from poisoning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Prop- erty inference from poisoning

Reference 33

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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.

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Observation f38006aa-2fe3-4451-a91f-06996ffc6097 · outbound

This paper cites Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag ¨uera y Arcas.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag ¨uera y Arcas

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-06T21:23:47.068589Z

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=2026-08-06T21:23:38.057589Z digest=sha256:4e72d894ca102b5097a99e5b6c06fd491c4c730f1880d62018c5cc24b579f6c8

Observation adcadde4-6556-424c-a37b-f4a90aba74fd · outbound

This paper cites Towards poisoning of deep learning algorithms with back-gradient optimization.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Towards poisoning of deep learning algorithms with back-gradient optimization

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:46.729207Z

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=2026-08-06T21:23:38.193278Z digest=sha256:20357114475faac6b637b9d7da2d4871aa8593f1bdb682150bee049dcd790412

Observation adce41fd-9a1f-42e1-aa49-4f4d911f0376 · outbound

This paper cites Compre- hensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Compre- hensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:46.415972Z

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=2026-08-06T21:23:38.277220Z digest=sha256:82986b3c31721c93c942d671821cde55aa9cecbfb647baf6f10ac44919023346

Observation 5d4502bd-7e35-4ad4-916d-0fabc8bb9aef · outbound

This paper cites On the difficulty of member- ship inference attacks.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning On the difficulty of member- ship inference attacks

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:46.071049Z

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=2026-08-06T21:23:38.425847Z digest=sha256:7fc3720468c67c4153888e6b502c9dd8dae0fc8aeb74c81f482dd87afef6c6b7

Observation 4a60c254-b382-4e3e-a41a-b79f8804db18 · outbound

This paper cites Deepsight: Mitigating backdoor at- tacks in federated learning through deep model inspection.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Deepsight: Mitigating backdoor at- tacks in federated learning through deep model inspection

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:45.752884Z

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=2026-08-06T21:23:38.560580Z digest=sha256:5821eed17d521de02257f7ab2d30bf86c3e3eda89d852422dfeb40c2eb11e3c8

Observation 5972613d-c5a6-4744-a7d5-e87faa376ffd · outbound

This paper cites Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:45.437896Z

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=2026-08-06T21:23:38.696833Z digest=sha256:5a90bc1a96dcbb08d84fceedcf5ca41f52711b68d3a0d8bff8886352a9e67aee

Observation 96b062d9-26f4-4276-b636-eeccea2c3e79 · outbound

This paper cites Membership inference attacks against machine learning models.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Membership inference attacks against machine learning models

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:45.156399Z

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=2026-08-06T21:23:38.829362Z digest=sha256:70f4e03fb55a883167116e909b5da778d9a752afba9cf39872a3881a7b023754

Observation d876fd69-03ba-4028-a9e2-1b96360dbd39 · outbound

This paper cites Membership inference attacks against adversarially robust deep learning models.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Membership inference attacks against adversarially robust deep learning models

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:44.831240Z

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=2026-08-06T21:23:38.921382Z digest=sha256:1f9ca6519c0b70308b70681ee0d71eda3a3db853d67ce35fe7a6f788ab18e362

Observation 7c0c72cb-e70a-42c2-b5b8-dfa4ba0876f9 · outbound

This paper cites Privacy risks of securing machine learning models against adversarial ex- amples.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Privacy risks of securing machine learning models against adversarial ex- amples

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:44.545989Z

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=2026-08-06T21:23:39.079949Z digest=sha256:a01fdf04fc0043ace873a552d63e005ce5b2bf1ce429b645816084c4e12adafe

Observation d6d228fa-fd37-4cde-b04d-f25e670916c2 · outbound

This paper cites Can You Really Backdoor Federated Learning?.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Can You Really Backdoor Federated Learning?

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T21:23:39.209655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:23:39.209655Z digest=sha256:072b57e9af1d3ed09a050760311e6b22e6bcf0f0f61f7487f558978927d3ab53

Observation 1a09e3b1-bc0f-445c-a091-eb935dcc4895 · outbound

This paper cites Data poisoning attacks against federated learning systems.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Data poisoning attacks against federated learning systems

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:44.350521Z

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=2026-08-06T21:23:39.349124Z digest=sha256:8b0799ab32ba407fa96008a0321a15297d8de9808b02330ce43d914bba5c92d2

Observation cffc2079-44e5-47ad-97ed-a3e271677ca4 · outbound

This paper cites Towards Demystifying Membership Inference Attacks.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Towards Demystifying Membership Inference Attacks

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T21:23:39.475330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:23:39.475330Z digest=sha256:2aeca98f562442fa7af039063dcd79a3bf162c88844bd3c1b56ebac6f268d95a

Observation 01c5c67b-23af-4912-b9d1-46b674bc5e1f · outbound

This paper cites Poisoning attacks and defenses to federated unlearning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Poisoning attacks and defenses to federated unlearning

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:44.108715Z

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=2026-08-06T21:23:39.598389Z digest=sha256:a070ed0c5f833bc1aaee5b965f33dfbb353764f71f27c23d8a0267bac45b1b8d

Observation a28e3ae1-e3bf-4518-9e47-725b547b0177 · outbound

This paper cites Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T21:23:39.710949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:23:39.710949Z digest=sha256:d3d404a88da9a487f4144397d069357cb3f333700c379236d593ec9219111fe4

Observation fd4dffde-bc5e-473e-b3b4-03728c9160dc · outbound

This paper cites Generalized Byzantine-tolerant SGD.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Generalized Byzantine-tolerant SGD

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T21:23:39.890974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:23:39.890974Z digest=sha256:c21bf71ff3d8ed243dcdb5710d8d46d55e182ca24c609f444f5c06233b374fe7

Observation 35635d96-f408-4c54-aa95-772f5d245a55 · outbound

This paper cites Phocas: dimensional Byzantine-resilient stochastic gradient descent.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Phocas: dimensional Byzantine-resilient stochastic gradient descent

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T21:23:39.992673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:23:39.992673Z digest=sha256:145b3a8072beb059170bb30bccc611c2859bca99cb1fd6b287835e4c4c782c03

Observation 3d38569a-6c0a-4cc9-b41c-f7d0e6b99f53 · outbound

This paper cites Fe- dredefense: Defending against model poisoning attacks for federated learning using model update reconstruction error.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Fe- dredefense: Defending against model poisoning attacks for federated learning using model update reconstruction error

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:43.797975Z

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=2026-08-06T21:23:40.136780Z digest=sha256:057fed03783aa806c90f79cddef9ad9f88b1b93df55c042f2ec5426fec50feda

Observation 01bc5592-c106-4422-9cd8-fd270fdb3208 · outbound

This paper cites Robust federated learning mitigates client-side train- ing data distribution inference attacks.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Robust federated learning mitigates client-side train- ing data distribution inference attacks

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:43.525781Z

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=2026-08-06T21:23:40.265089Z digest=sha256:38e144a37c3489f93e29de5f894604df7f3c0d6cdc1f49ebfc4f064d24d7e759

Observation 05a32bb4-23da-4fbb-a321-af38ef244d51 · outbound

This paper cites Federated machine learning: Concept and applications.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Federated machine learning: Concept and applications

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:43.216575Z

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=2026-08-06T21:23:40.393252Z digest=sha256:b77735519bc977df36414748ac0d745dd9432458cf77c08e0d7f298912dafcf2

Observation 86e6bcfd-61a0-42a8-9440-68afcad57c4e · outbound

This paper cites Enhanced membership in- ference attacks against machine learning models.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Enhanced membership in- ference attacks against machine learning models

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:42.967113Z

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=2026-08-06T21:23:40.495500Z digest=sha256:c5bfeec76822d928a32d297f6587a9851ce939e8348516d76d9bd30a0c404705

Observation d197e004-de7a-4757-8e44-75f23fbda67b · outbound

This paper cites Privacy risk in machine learning: Analyzing the connection to overfitting.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Privacy risk in machine learning: Analyzing the connection to overfitting

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:42.701810Z

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=2026-08-06T21:23:40.557083Z digest=sha256:5a034ce7808d28c0c482b3edb9303e9fd6c6549f5a48955736c5a4f26d5bbeef

Observation 46139d39-32b7-41b0-8ec3-2d34cc18ba44 · outbound

This paper cites Byzantine-robust distributed learning: Towards op- timal statistical rates.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Byzantine-robust distributed learning: Towards op- timal statistical rates

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:42.413805Z

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=2026-08-06T21:23:40.654407Z digest=sha256:fb5101796f85da8f304c39a50f0ebdfc4aa89c15f96f908eb58b616260dcd6b9

Observation 8f89a9b6-b60e-45ae-8984-52b88a5ea233 · outbound

This paper cites Poisoning federated recommender systems with fake users.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Poisoning federated recommender systems with fake users

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:42.137262Z

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=2026-08-06T21:23:40.806811Z digest=sha256:51a51ce3449e02c75b9a767520bdca76251ae20c206f081ee2616df41b2a764d

Observation 69fa18d1-d3d7-4b7d-8336-de4dc57c8739 · outbound

This paper cites Agrevader: Poisoning membership inference against byzantine-robust federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Agrevader: Poisoning membership inference against byzantine-robust federated learning

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:41.877820Z

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=2026-08-06T21:23:40.931123Z digest=sha256:08729dda682e21ffcbbc56ef34ec4c09c8edb87b1f8b3c2bd810e4d05d9f767e

Observation c85cf7ea-a83f-42c6-80b3-a0c90d37d49f · outbound

This paper cites Pn−b i=b+1(θi − ω) n − 2b #2 ≤ max   .

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Pn−b i=b+1(θi − ω) n − 2b #2 ≤ max   

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:23:41.626723Z

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=2026-08-06T21:23:41.069355Z digest=sha256:24cfeefbe3cd500d58a46be354601fe0a1df86b7c56ffa83c83dc13d1a049497

Pith citing papers

No inbound Pith citation observations are available.