Paper Citation Record · LEDGER

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning

As of 24 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2502.04106.

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

pith.paper-citation-record.v1
2502.04106 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:33:38.967571Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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 exact1
verified fuzzy25
unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 11760e54-99a9-4bf8-a1ae-72498a1bbe10 · outbound

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

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Communication-efficient learning of deep networks from decentralized data,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.823269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41a946ea-e74d-4dae-95d6-697f42f5c49d · outbound

This paper cites Towards Federated Learning at Scale: System Design.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Towards Federated Learning at Scale: System Design

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.827758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1545f046-a3c5-4925-a334-b1f57f4a2244 · outbound

This paper cites Project adam: Building an efficient and scalable deep learning training system,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Project adam: Building an efficient and scalable deep learning training system,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.522084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f10b4e2a-25b1-40f3-b108-abe5fba3753a · outbound

This paper cites Deep leakage from gradients,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Deep leakage from gradients,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.834694Z

Source-reported events for the cited work

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Observation e0b86e0f-37f4-4ee4-93f9-561d175ba751 · outbound

This paper cites QBI: Quantile-Based Bias Initialization for Efficient Private Data Reconstruction in Federated Learning.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning QBI: Quantile-Based Bias Initialization for Efficient Private Data Reconstruction in Federated Learning

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:33:39.141383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 02df3065-34e7-4163-8139-05197c56bb93 · outbound

This paper cites R-gap: Recursive gradient attack on privacy,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning R-gap: Recursive gradient attack on privacy,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.504111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2f0e2ae4-8049-4515-9e3d-e8be3808c9bb · outbound

This paper cites Using highly compressed gradients in federated learning for data reconstruction attacks,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Using highly compressed gradients in federated learning for data reconstruction attacks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.494128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a615ef35-48d0-49cf-9f0a-a7109915a332 · outbound

This paper cites Gradient obfuscation gives a false sense of security in federated learning,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Gradient obfuscation gives a false sense of security in federated learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.483147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 383f9427-5476-4c80-bcef-cc12d44f4f49 · outbound

This paper cites When the curious abandon honesty: Federated learning is not private,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning When the curious abandon honesty: Federated learning is not private,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.472150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation db220d53-79d2-4acf-837c-388d9701743e · outbound

This paper cites Loki: Large-scale data reconstruction attack against federated learning through model manipulation,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Loki: Large-scale data reconstruction attack against federated learning through model manipulation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.460359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fbfdd31d-271b-4b7d-8a5f-b5f90e5d74a8 · outbound

This paper cites Robbing the fed: Directly obtaining private data in federated learn- ing with modified models,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Robbing the fed: Directly obtaining private data in federated learn- ing with modified models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.448296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8ebbd8c0-ccaf-4a72-85b1-baf02330bff0 · outbound

This paper cites iDLG: Improved Deep Leakage from Gradients.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning iDLG: Improved Deep Leakage from Gradients

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.862873Z

Source-reported events for the cited work

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Observation 1ae9f81b-60e1-4f00-8b02-9c30e6b910ff · outbound

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

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Inverting gradients-how easy is it to break privacy in federated learning?

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.436205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 719be77b-05c8-4015-ba86-a1a42c0be548 · outbound

This paper cites See through gradients: Image batch recovery via gradinversion,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning See through gradients: Image batch recovery via gradinversion,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.426084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fac88926-a08f-4458-ab30-adebe22fcc64 · outbound

This paper cites SoK: On Gradient Leakage in Federated Learning.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning SoK: On Gradient Leakage in Federated Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.874839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 442bee64-737d-441a-8251-37557ec53f43 · outbound

This paper cites Fishing for user data in large-batch federated learning via gradient magnification,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Fishing for user data in large-batch federated learning via gradient magnification,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.415807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 972cd090-7cb2-4e00-97a9-d432addfa421 · outbound

This paper cites Hiding in plain sight: Disguising data stealing attacks in federated learning,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Hiding in plain sight: Disguising data stealing attacks in federated learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.404674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f0b46a24-06f1-474c-8339-85789c736b5e · outbound

This paper cites Gradient-based learning applied to document recognition,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Gradient-based learning applied to document recognition,

Reference 18

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Source-reported events for the cited work

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Observation 5cd89b63-9105-4219-9f6a-ad8091f65cfe · outbound

This paper cites User-level label leakage from gradients in federated learning,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning User-level label leakage from gradients in federated learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.385136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 45f1046e-f424-434d-99c9-c9fcf807675a · outbound

This paper cites Instance-wise batch label restoration via gradients in federated learning,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Instance-wise batch label restoration via gradients in federated learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.372560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 04ca1901-7e9d-4ab6-a65b-e9ba627fb05f · outbound

This paper cites Towards eliminating hard label constraints in gradient inversion attacks,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Towards eliminating hard label constraints in gradient inversion attacks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.360919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bf2478db-2e55-41cd-b597-4c7f55d132f6 · outbound

This paper cites Gradient inversion with generative image prior,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Gradient inversion with generative image prior,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.349547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7f863df8-2801-429a-b8f2-9371adde6cb1 · outbound

This paper cites Auditing privacy defenses in federated learning via generative gradient leakage,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Auditing privacy defenses in federated learning via generative gradient leakage,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.340035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e715db7f-a194-4500-bc08-fed27b245409 · outbound

This paper cites Federated learning vulnerabilities: Privacy attacks with denoising diffusion probabilistic models,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Federated learning vulnerabilities: Privacy attacks with denoising diffusion probabilistic models,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.330036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1e0f95ed-a7ab-4078-985a-d0acaca44b05 · outbound

This paper cites Compromise privacy in large- batch federated learning via malicious model parameters,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Compromise privacy in large- batch federated learning via malicious model parameters,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.319546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 029995bf-70ea-42d0-9f1b-ed6c0586c765 · outbound

This paper cites Eluding secure aggregation in federated learning via model inconsistency,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Eluding secure aggregation in federated learning via model inconsistency,

Reference 26

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Source-reported events for the cited work

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Observation 120659ee-bb10-416a-b1b9-83c5a78d74b1 · outbound

This paper cites Rectified linear units improve restricted boltzmann machines,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Rectified linear units improve restricted boltzmann machines,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.914122Z

Source-reported events for the cited work

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Observation c208699f-ae5e-43ca-a1f1-ba00576da1d0 · outbound

This paper cites Transformers: State-of-the-art natural language processing,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Transformers: State-of-the-art natural language processing,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.292223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a1168cfc-fe1a-45b8-ae15-9ded37165115 · outbound

This paper cites Kaggle: Your machine learning and data science community,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Kaggle: Your machine learning and data science community,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.279858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 74756f40-6df6-4662-9b14-c05c0c8c9fbd · outbound

This paper cites Openml: Networked science in machine learning,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Openml: Networked science in machine learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.267599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8fd48e1d-e11c-452b-8daf-29f17b486ae9 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Understanding the difficulty of training deep feedforward neural networks,

Reference 31

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Source-reported events for the cited work

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Observation 4e4b8493-5004-4807-8fd3-e8336831d27a · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.930517Z

Source-reported events for the cited work

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Observation 5a020be0-c1af-4edc-821d-9c6d6a11f118 · outbound

This paper cites Rudin, Principles of Mathematical Analysis , ser.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Rudin, Principles of Mathematical Analysis , ser

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.241525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 58716ed9-d8cc-4abc-9ffe-cdf7d74a1339 · outbound

This paper cites Bartle and D.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Bartle and D

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.230510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d8eb215b-818c-4ee1-bff5-5b81a70cc636 · outbound

This paper cites Deep residual learning for image recognition,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Deep residual learning for image recognition,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.945845Z

Source-reported events for the cited work

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Observation d8e6d975-e764-4b02-9c6c-e01f236bb1e8 · outbound

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

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Learning multiple layers of features from tiny images,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.949872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9147c79-f671-4fbf-b682-e9e240271a8e · outbound

This paper cites Tiny imagenet visual recognition challenge,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Tiny imagenet visual recognition challenge,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.953873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 488e229b-8a26-405e-bdf2-3ef92a5f7b2d · outbound

This paper cites Image quality metrics: Psnr vs. ssim,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Image quality metrics: Psnr vs. ssim,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.957561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 848afd55-ef20-4104-9cd3-9ce1f09a4673 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning The unreasonable effectiveness of deep features as a perceptual metric,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.961218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe4fae5d-9d1f-49c6-a39b-d9699577197a · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Image quality assessment: from error visibility to structural similarity,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.964434Z

Source-reported events for the cited work

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Observation f4fafa09-800d-4f0c-8ff2-53f14b038947 · outbound

This paper cites Visualizing data using t-sne.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Visualizing data using t-sne

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.164541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2cc322c1-06d4-4262-87e3-3bad6d83f5b9 · outbound

This paper cites Available: https://books.google.com.sg/books?id= YawbAAAAQBAJ.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning Available: https://books.google.com.sg/books?id= YawbAAAAQBAJ

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:33:39.219636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

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