Paper Citation Record · LEDGER

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution

As of 14 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2411.10673.

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

pith.paper-citation-record.v1
2411.10673 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

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measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation fd46f89f-5cd0-4ef5-a162-672ae8756868 · outbound

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

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Communication-efficient learning of deep networks from decentral- ized data,

Reference 1

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Observation 4000d4dd-4475-492e-8584-45215d5a30ca · outbound

This paper cites Membership inference attacks against machine learning models,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Membership inference attacks against machine learning models,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:26.410907Z

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Observation 950ab44b-fe8c-451e-af27-7514afdbc196 · outbound

This paper cites Exploiting unintended feature leakage in collaborative learning,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Exploiting unintended feature leakage in collaborative learning,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:26.416036Z

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Observation 0bf9e78a-6b7e-442f-83be-8349c60dd735 · outbound

This paper cites Inverting gradients-how easy is it to break privacy in federated learning?.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Inverting gradients-how easy is it to break privacy in federated learning?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:26.422835Z

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Observation b968dd4f-642a-4ee7-8c28-82caf813761a · outbound

This paper cites Wild patterns reloaded: A survey of machine learning security against training data poisoning,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Wild patterns reloaded: A survey of machine learning security against training data poisoning,

Reference 5

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Observation 62dac89c-c72d-4816-880c-a39fb2dcff24 · outbound

This paper cites A novel data poisoning attack in federated learning based on inverted loss function,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution A novel data poisoning attack in federated learning based on inverted loss function,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:27.267562Z

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Observation f5a4864d-8e92-4ede-bf16-744da77d0f7a · outbound

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

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Local model poisoning attacks to {Byzantine-Robust} federated learning,

Reference 7

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Observation 0f52a96f-8fb8-45c3-95c8-a7091ed57a86 · outbound

This paper cites Mpaf: Model poisoning attacks to federated learning based on fake clients,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Mpaf: Model poisoning attacks to federated learning based on fake clients,

Reference 8

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Observation ed65865a-4f78-4ebe-b36b-5ab1d6a1ff1e · outbound

This paper cites Backdoor attacks and defenses in federated learning: State-of-the-art, taxonomy, and future directions,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Backdoor attacks and defenses in federated learning: State-of-the-art, taxonomy, and future directions,

Reference 9

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Observation 9f54fb6c-b62a-42b0-8a63-77e97b93dcad · outbound

This paper cites How to backdoor federated learning,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution How to backdoor federated learning,

Reference 10

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Source-reported events for the cited work

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Observation c6428ebc-b91e-4c0f-9f92-6c50f878489e · outbound

This paper cites Manipulating the byzantine: Op- timizing model poisoning attacks and defenses for federated learning,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Manipulating the byzantine: Op- timizing model poisoning attacks and defenses for federated learning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:26.458591Z

Source-reported events for the cited work

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Observation 377e386a-671a-4e9c-8602-aa7d5109501b · outbound

This paper cites Ma- chine learning with adversaries: Byzantine tolerant gradient descent,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Ma- chine learning with adversaries: Byzantine tolerant gradient descent,

Reference 12

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Observation 6d47eddf-3a2b-4288-b7ac-e124311cd031 · outbound

This paper cites Byzantine-robust dis- tributed learning: Towards optimal statistical rates,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Byzantine-robust dis- tributed learning: Towards optimal statistical rates,

Reference 13

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Observation 9b06aaf0-d4c8-4b14-b791-19027c882fc4 · outbound

This paper cites The hidden vulnerability of dis- tributed learning in byzantium,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution The hidden vulnerability of dis- tributed learning in byzantium,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:27.179946Z

Source-reported events for the cited work

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Observation 64d31898-b3e7-40de-8b4a-c19fd047a614 · outbound

This paper cites Auror: Defending against poisoning attacks in collaborative deep learning systems,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Auror: Defending against poisoning attacks in collaborative deep learning systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:27.165403Z

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Observation 37d7987c-3190-4ef2-a823-e09cd0783b67 · outbound

This paper cites Privacy- enhanced federated learning against poisoning adversaries,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Privacy- enhanced federated learning against poisoning adversaries,

Reference 16

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Observation e8dee3ee-b34b-4649-9f8f-c6b449ddfba8 · outbound

This paper cites Shieldfl: Mitigating model poisoning attacks in privacy-preserving federated learning,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Shieldfl: Mitigating model poisoning attacks in privacy-preserving federated learning,

Reference 17

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Observation a615ca3d-92d7-4568-bfb4-ba15b1b8fa7e · outbound

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

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Reference 18

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Observation 38981272-d84b-473d-93c8-82225e86189b · outbound

This paper cites Protecting federated learning from extreme model poisoning attacks via multidimensional time series anomaly detection,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Protecting federated learning from extreme model poisoning attacks via multidimensional time series anomaly detection,

Reference 19

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Observation 99643ece-0cf0-49d0-866d-2ed7a9185507 · outbound

This paper cites Fldetector: Defending federated learning against model poisoning attacks via detecting ma- licious clients,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Fldetector: Defending federated learning against model poisoning attacks via detecting ma- licious clients,

Reference 20

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Observation 2216490a-26dd-4db4-8c5c-9bf4e52016f1 · outbound

This paper cites Deep gen- erative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Deep gen- erative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models,

Reference 21

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Observation 5a3995ce-fc75-4f93-9ed0-6dc87e31b9bc · outbound

This paper cites Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 22

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Observation 7aa16c38-9b85-48b3-931d-f237760d2a28 · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 23

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This paper cites Convergence analysis of two-layer neural net- works with relu activation,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Convergence analysis of two-layer neural net- works with relu activation,

Reference 24

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Observation 80ea4658-9103-4cbf-a321-19bfcd2c7b8c · outbound

This paper cites A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 25

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Observation 703ef0c2-a2a3-4ec5-88b7-82f4c0bd57c3 · outbound

This paper cites On the Convergence of FedAvg on Non-IID Data.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution On the Convergence of FedAvg on Non-IID Data

Reference 26

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Observation ece77aa2-5136-41a2-a4e9-8dfbeb5886f9 · outbound

This paper cites Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense

Reference 27

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Observation bb9ba90f-84d5-4575-b2a6-2be95a7ea0df · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 28

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Observation c67ff1af-ebfb-49b2-8223-beaffc120c82 · outbound

This paper cites A dual stealthy backdoor: From both spatial and frequency perspectives,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution A dual stealthy backdoor: From both spatial and frequency perspectives,

Reference 29

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Observation 4e1ef1d8-3cad-44b0-9591-4ebc0e99e795 · outbound

This paper cites Narcissus: A practical clean-label backdoor attack with limited information,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Narcissus: A practical clean-label backdoor attack with limited information,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:27.068253Z

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Observation 82c5fadd-c72e-4ca5-8419-1b3ed2a192cf · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Imagenet: A large-scale hierarchical image database,

Reference 31

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Observation 6a56b19b-2a5f-47be-a0b0-e12e9e8f0b61 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Learning Transferable Visual Models From Natural Language Supervision

Reference 32

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This paper cites Deep residual learning for image recognition,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Deep residual learning for image recognition,

Reference 33

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Observation 5144f810-9bd0-46f7-8693-d5001d3daaa0 · outbound

This paper cites Baruch, G.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Baruch, G

Reference 34

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Observation b309e927-63f5-42e0-8a08-73ebbfe7a0da · outbound

This paper cites Adam: A method for stochastic optimization,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Adam: A method for stochastic optimization,

Reference 35

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This paper cites A stochastic approximation method,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution A stochastic approximation method,

Reference 36

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Observation 93fe62e9-0faa-4fa8-a5d5-358679bf6f3d · outbound

This paper cites Representations of quasi-newton matrices and their use in limited memory methods,.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Representations of quasi-newton matrices and their use in limited memory methods,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:26.995566Z

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This paper cites Algorithm 1 Execution of VERT.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Algorithm 1 Execution of VERT

Reference 40

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Observation 3eaf7cf6-f679-4596-9fb0-7f121f1dec00 · outbound

This paper cites Let the partial derivative is 0, then: ∂Φ(A; B; fpred; fproj ) ∂A = 0.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Let the partial derivative is 0, then: ∂Φ(A; B; fpred; fproj ) ∂A = 0

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:32:26.962710Z

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How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Unresolved cited work

Reference 42

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How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Unresolved cited work

Reference 43

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Observation 1f2d3c5a-0b5f-4c13-83d1-e7090ed548be · outbound

This paper cites TABLE 4: The defense effectiveness of different defenses against large-scale model poisoning attacks in non-IID scenarios.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution TABLE 4: The defense effectiveness of different defenses against large-scale model poisoning attacks in non-IID scenarios

Reference 44

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Observation 4f6e31a6-6c93-445b-ac3f-73466b3cbe85 · outbound

This paper cites Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information

Reference 2022

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Observation 5a0fdac8-85a1-4d7e-9b1c-c58ccebc26de · outbound

This paper cites A Dual Stealthy Backdoor: From Both Spatial and Frequency Perspectives.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution A Dual Stealthy Backdoor: From Both Spatial and Frequency Perspectives

Reference 2023

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T19:32:26.706470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.