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Paper Citation Record · LEDGER

Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2202.00580.

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

pith.paper-citation-record.v1
2202.00580 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:26:17.107366Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T04:39:35.030793Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 276c7e10-e486-43f3-b999-a0e9fe32c6f8 · inbound

Hidden Data Privacy Breaches in Federated Learning cites this paper.

Hidden Data Privacy Breaches in Federated Learning Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-12T11:26:17.107366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:26:17.107366Z digest=sha256:35f4704787348962a2ba9a58181a4d732f51d6baedeec1b33a3f25e8e8e604c9

Observation 8b329722-6385-4f64-9a6b-645d0594cf5e · inbound

Gradient Inversion Attack on Graph Neural Networks cites this paper.

Gradient Inversion Attack on Graph Neural Networks Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-12T10:17:09.994808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T10:17:09.994808Z digest=sha256:2bd1c8885d37fcf4b01af25e04eb6f921e87f35d51a266beb2897f3d43e4c789

Observation 72fc3d53-8dcf-4938-aa96-129952128bb8 · inbound

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling cites this paper.

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-10T14:05:45.510473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:05:45.510473Z digest=sha256:e8aaf97e79ce772f0db5aec3fb12e06cde740dd32c52d005d70fc5ef1d8a3ff0

Observation a72c9911-f588-4ec4-97d8-d55fcc1b39fa · inbound

FLRSP: Privacy-Preserving Federated Learning Using Randomly Selected Model Parameters cites this paper.

FLRSP: Privacy-Preserving Federated Learning Using Randomly Selected Model Parameters Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:51:05.423372Z

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.

source=pdf_text observed=2026-05-09T14:56:09.933026Z digest=sha256:5df5fb923abc14d149211f8f49952609c3dcf83c96a208f5ccb7c9d1a81797b1

Observation 31ec78ff-038b-4d9a-915f-f05ca0e0aef3 · inbound

From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning cites this paper.

From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

Reference 57

Resolution
verified exact
arxiv_id, observed 2026-07-04T04:39:35.032288Z

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.

source=pdf_text observed=2026-06-26T16:51:07.028013Z digest=sha256:b64dfde3971eda1a0512639a157effac9e756e763cb3022fe45360b46834df69