Paper Citation Record · LEDGER

A New Federated Learning Framework Against Gradient Inversion Attacks

As of 17 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2412.07187.

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

pith.paper-citation-record.v1
2412.07187 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:08:54.628478Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:58:40.443324Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy4
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

1
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation 8b43fb9f-bc88-460f-8412-27c7e286f3cd · outbound

This paper cites IEEE Transactions on Big Data.

A New Federated Learning Framework Against Gradient Inversion Attacks IEEE Transactions on Big Data

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:54.870404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 231c2504-e525-45b0-8e78-297fafa08b06 · outbound

This paper cites International Journal of Intelligent Systems , 37(11): 9373– 9389.

A New Federated Learning Framework Against Gradient Inversion Attacks International Journal of Intelligent Systems , 37(11): 9373– 9389

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:54.851024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5335fad3-e1dc-4db7-b583-c01ebd2e7865 · outbound

This paper cites Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective.

A New Federated Learning Framework Against Gradient Inversion Attacks Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T19:08:54.738330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f00741ee-7e4d-4505-a4c8-d8f22b69773d · outbound

This paper cites Salvaging Federated Learning by Local Adaptation.

A New Federated Learning Framework Against Gradient Inversion Attacks Salvaging Federated Learning by Local Adaptation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T19:08:54.614786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e458707d-53ff-48b0-b24a-86274ade0241 · outbound

This paper cites iDLG: Improved Deep Leakage from Gradients.

A New Federated Learning Framework Against Gradient Inversion Attacks iDLG: Improved Deep Leakage from Gradients

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T19:08:54.628478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10ab8a09-79f6-4657-bf4b-55ba5e2379c8 · outbound

This paper cites A Framework for Evaluating Gradient Leakage Attacks in Federated Learning.

A New Federated Learning Framework Against Gradient Inversion Attacks A Framework for Evaluating Gradient Leakage Attacks in Federated Learning

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-11T19:08:54.608669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88832eb9-cf6d-46c7-88ce-1476abd94436 · outbound

This paper cites Gradient Leakage Defense with Key-Lock Module for Federated Learning.

A New Federated Learning Framework Against Gradient Inversion Attacks Gradient Leakage Defense with Key-Lock Module for Federated Learning

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-11T19:08:54.593835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f651cf50-90db-48d5-ac77-d7d48191282e · outbound

This paper cites Differentially Private Federated Learning: A Client Level Perspective.

A New Federated Learning Framework Against Gradient Inversion Attacks Differentially Private Federated Learning: A Client Level Perspective

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T19:08:54.562640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d4c1a02-eab4-4b9d-a755-a931b63cbeea · outbound

This paper cites CINIC-10 is not ImageNet or CIFAR-10.

A New Federated Learning Framework Against Gradient Inversion Attacks CINIC-10 is not ImageNet or CIFAR-10

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T19:08:54.554386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bb28fba-883b-42cd-9e4f-6d059ad3f296 · outbound

This paper cites In International Conference on Ma- chine Learning, 1945–1962.

A New Federated Learning Framework Against Gradient Inversion Attacks In International Conference on Ma- chine Learning, 1945–1962

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:54.889435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bdae5f8c-9889-4c62-b562-432efd162283 · outbound

This paper cites In 32nd USENIX Security Symposium (USENIX Security 23), 6381–6398.

A New Federated Learning Framework Against Gradient Inversion Attacks In 32nd USENIX Security Symposium (USENIX Security 23), 6381–6398

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:08:54.831526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b4cf42ed-3e2f-4003-8124-dcb99b54f9c6 · outbound

This paper cites Selective Aggregation for Low-Rank Adaptation in Federated Learning.

A New Federated Learning Framework Against Gradient Inversion Attacks Selective Aggregation for Low-Rank Adaptation in Federated Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T19:08:54.569395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 754e0df1-75f4-40f9-bc86-180f9560b66a · inbound

Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey cites this paper.

Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey A New Federated Learning Framework Against Gradient Inversion Attacks

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-09T21:58:43.702057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.