Paper Citation Record · LEDGER

Poison to Detect: Detection of Targeted Overfitting in Federated Learning

As of 8 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2509.11974.

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

pith.paper-citation-record.v1
2509.11974 v3

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:44:27.686079Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

42 of 42 outbound references displayed

verified exact0
verified fuzzy0
unresolved42
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b1ddf1f2-b099-48c0-bbb3-86e80e9416a2 · outbound

This paper cites Baffle: Backdoor detection via feedback-based federated learning.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Baffle: Backdoor detection via feedback-based federated learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:24.356871Z

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Unavailable: canonical work link unavailable.

Observation c9da7df6-a00e-4f21-ab35-2bf7b27a9760 · outbound

This paper cites How to backdoor federated learning.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning How to backdoor federated learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:24.412845Z

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Observation 0dfdc32c-336a-4eb2-95e4-c2fb2fc81021 · outbound

This paper cites Reconstructing training data with informed adversaries.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Reconstructing training data with informed adversaries

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:24.488746Z

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Observation aaa62736-c0c5-4727-8fce-26a4705e81a4 · outbound

This paper cites Reconstruction attacks on machine unlearning: Simple models are vulnerable.Advances in Neural Information Processing Systems, 37:104995–105016, 2024.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Reconstruction attacks on machine unlearning: Simple models are vulnerable.Advances in Neural Information Processing Systems, 37:104995–105016, 2024

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:24.533283Z

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Observation 85d596fd-78b9-448e-b49b-a8820cfd5412 · outbound

This paper cites Poisoning attacks against support vector machines.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Poisoning attacks against support vector machines

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:24.597537Z

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Observation 47b73e4d-c7d6-44c3-84e9-46a3707317cb · outbound

This paper cites Machine learning with adver- saries: Byzantine tolerant gradient descent.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Machine learning with adver- saries: Byzantine tolerant gradient descent

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:24.658937Z

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Observation 2c7610e2-c6b7-4e58-853c-19f982af5bad · outbound

This paper cites Federated learning attacks and defenses: A survey.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Federated learning attacks and defenses: A survey

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:24.720242Z

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Observation ad02c309-f576-4710-989d-6f63cec1c178 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].IEEE signal processing magazine, 29(6):141–142, 2012.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning The mnist database of handwritten digit images for machine learning research [best of the web].IEEE signal processing magazine, 29(6):141–142, 2012

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:24.777357Z

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Observation 5b80a8dc-0737-470c-9b46-e000c55ae549 · outbound

This paper cites Local model poisoning attacks to Byzantine-Robust federated learning.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Local model poisoning attacks to Byzantine-Robust federated learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:24.868196Z

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Observation 8d11c0e2-a2f5-4495-b060-2e890fafe40c · outbound

This paper cites Model inversion attacks that exploit confidence infor- mation and basic countermeasures.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Model inversion attacks that exploit confidence infor- mation and basic countermeasures

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:24.965785Z

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Observation ec5d26cb-3ae3-4681-8daa-8630f37888b1 · outbound

This paper cites A novel data poisoning attack in federated learning based on inverted loss function.Computers & Security, 130:103270, 2023.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning A novel data poisoning attack in federated learning based on inverted loss function.Computers & Security, 130:103270, 2023

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.034138Z

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Observation 3185a756-0949-4746-bb08-0c2b55b0e660 · outbound

This paper cites Deep residual learning for image recognition.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Deep residual learning for image recognition

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.153404Z

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Observation c6b2399e-9bd6-492b-9d86-01ddd030fd88 · outbound

This paper cites an unresolved cited work.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.226812Z

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Observation 66004497-8def-4f5d-aa36-d1c8ff2c5d6d · outbound

This paper cites an unresolved cited work.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.273175Z

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Observation 652ba506-895d-4af8-9d6c-b87b3b196be6 · outbound

This paper cites Loadaboost: Loss-based adaboost federated machine learning with reduced computational complexity on iid and non-iid intensive care data.Plos one, 15(4):e0230706, 2020.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Loadaboost: Loss-based adaboost federated machine learning with reduced computational complexity on iid and non-iid intensive care data.Plos one, 15(4):e0230706, 2020

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.344840Z

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Observation dcb4696e-306c-40dd-b191-912707932ea2 · outbound

This paper cites Advances and open problems in federated learning.Foundations and trends® in machine learning, 14(1–2):1–210, 2021.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Advances and open problems in federated learning.Foundations and trends® in machine learning, 14(1–2):1–210, 2021

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.418980Z

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Observation 04e1260a-f441-45f8-a6f7-79659b6cd150 · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learning.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Scaffold: Stochastic controlled averaging for federated learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.465675Z

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Observation 9b80ed15-4625-4d2a-be78-5b64713b26b9 · outbound

This paper cites Schaefer.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Schaefer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.514625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0761e092-50cd-46cf-b99a-ecb9702402e8 · outbound

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

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Learning multiple layers of features from tiny images

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.589952Z

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Unavailable: canonical work link unavailable.

Observation 7a23c01a-3994-47f0-8a25-146f60f2de30 · outbound

This paper cites Learning multiple layers of features from tiny images.(2009), 2009.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Learning multiple layers of features from tiny images.(2009), 2009

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.684654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f0c1db1-e934-4b85-9a99-8262f9643185 · outbound

This paper cites Data poisoning attacks on factorization-based collaborative filtering.Advances in neural information processing systems, 29, 2016.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Data poisoning attacks on factorization-based collaborative filtering.Advances in neural information processing systems, 29, 2016

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.756096Z

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Unavailable: canonical work link unavailable.

Observation 16160177-e856-41b3-b9c8-a195e9b99715 · outbound

This paper cites Federated learning: Challenges, methods, and future directions.IEEE signal processing magazine, 37(3):50–60, 2020.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Federated learning: Challenges, methods, and future directions.IEEE signal processing magazine, 37(3):50–60, 2020

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.828733Z

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Unavailable: canonical work link unavailable.

Observation b9ce9b48-3158-4f0f-9742-d4e74c77adad · outbound

This paper cites A blockchain-based decentralized federated learning framework with committee consensus.IEEE Network, 35(1):234–241, 2020.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning A blockchain-based decentralized federated learning framework with committee consensus.IEEE Network, 35(1):234–241, 2020

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.898438Z

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Unavailable: canonical work link unavailable.

Observation b3694855-e0af-4d4b-a6c7-a3c0fcb966f6 · outbound

This paper cites On the over-memorization during natural, robust and catastrophic overfitting.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning On the over-memorization during natural, robust and catastrophic overfitting

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:25.994865Z

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Observation a784d84b-7721-4abf-b2db-e2dd2f2b292e · outbound

This paper cites Springer International Publishing, Cham, 2020.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Springer International Publishing, Cham, 2020

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:26.097672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85ba3964-e6e3-474c-af03-12a673ac0ce0 · outbound

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

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Communication- efficient learning of deep networks from decentralized data

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:26.194887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d860451f-cc8c-40ae-bc33-d385bac5af2c · outbound

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

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:26.302350Z

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Observation 0364b8fd-cceb-435c-bd6c-c16e3bed3772 · outbound

This paper cites Dataset reconstruction attack against language models.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Dataset reconstruction attack against language models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:26.416097Z

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Observation 545bc633-ddc9-447c-a42d-8e794f251603 · outbound

This paper cites Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Koneˇcný, Sanjiv Kumar, and Hugh Brendan McMahan.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Koneˇcný, Sanjiv Kumar, and Hugh Brendan McMahan

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:26.496515Z

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Observation 74a4d7f6-2c0b-40d8-8de8-5c014d6ce422 · outbound

This paper cites Fetchsgd: Communication-efficient federated learning with sketching.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Fetchsgd: Communication-efficient federated learning with sketching

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:26.604259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9707490-ea9d-47f1-a2bf-676f848f0677 · outbound

This paper cites Membership inference attacks against machine learning models.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Membership inference attacks against machine learning models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:26.696935Z

Source-reported events for the cited work

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Observation 5f8e3db9-1b2e-40fa-8ce6-50da1715a604 · outbound

This paper cites Data poisoning attacks against federated learning systems.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Data poisoning attacks against federated learning systems

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:26.752882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 602e4b50-460b-4c3a-9e6b-84f6914ae097 · outbound

This paper cites Beyond inferring class representatives: User-level privacy leakage from federated learning.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Beyond inferring class representatives: User-level privacy leakage from federated learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:26.838704Z

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Unavailable: canonical work link unavailable.

Observation 2710a76f-78b1-48e2-b879-70a568f76d6f · outbound

This paper cites Naughton.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Naughton

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:26.917504Z

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Unavailable: canonical work link unavailable.

Observation 12df26d2-e570-4034-bfbc-214e2c263d65 · outbound

This paper cites Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.Scientific Data, 10(1):41, 2023.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.Scientific Data, 10(1):41, 2023

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:27.001357Z

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Unavailable: canonical work link unavailable.

Observation a1c4d22f-4830-4347-8428-326f09f15e48 · outbound

This paper cites Robust federated learning with noisy labels.IEEE Intelligent Systems, 37(2):35–43, 2022.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Robust federated learning with noisy labels.IEEE Intelligent Systems, 37(2):35–43, 2022

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:27.109418Z

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Observation c216426f-4216-47b5-9cb7-c7c7cbb774cb · outbound

This paper cites Deep learning model inversion attacks and defenses: a comprehensive survey.Artificial Intelligence Review, 58(8):1–52, 2025.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Deep learning model inversion attacks and defenses: a comprehensive survey.Artificial Intelligence Review, 58(8):1–52, 2025

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:27.218191Z

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Observation 1a65abb4-6410-4675-b9f2-60107c0568e2 · outbound

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

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Privacy risk in machine learning: Analyzing the connection to overfitting

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:27.323994Z

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Observation de3f1f51-247d-43d6-a7b6-0a4a4e22c382 · outbound

This paper cites Curse or redemption? how data heterogeneity affects the robustness of federated learning.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Curse or redemption? how data heterogeneity affects the robustness of federated learning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:27.410218Z

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Unavailable: canonical work link unavailable.

Observation 8be0ac16-f47d-4e9e-baff-735daee0f0e5 · outbound

This paper cites A Survey on Class Imbalance in Federated Learning.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning A Survey on Class Imbalance in Federated Learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:27.497858Z

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Unavailable: canonical work link unavailable.

Observation aaaf2108-cdde-4df4-a769-d029234f1df7 · outbound

This paper cites Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:27.570164Z

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Observation 85464e48-e712-4bbe-bb21-0a659ec74480 · outbound

This paper cites FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk.

Poison to Detect: Detection of Targeted Overfitting in Federated Learning FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T16:44:27.686079Z

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Pith citing papers

No inbound Pith citation observations are available.