Paper Citation Record · LEDGER

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning

As of 3 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2510.08750.

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

pith.paper-citation-record.v1
2510.08750 v2

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T08:30:08.237450Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T15:56:55.547579Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-30T16:04:53.237820Z

Reference resolution

15 of 15 outbound references displayed

verified exact1
verified fuzzy8
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e94c0064-d374-43c5-aea0-d5e90cd8408b · outbound

This paper cites The Llama 3 Herd of Models.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning The Llama 3 Herd of Models

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T08:31:06.777719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6f91b24c-061f-4c87-802c-dc23a25c8d1b · outbound

This paper cites an unresolved cited work.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-05-18T08:31:07.082530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b5b60b83-0a2d-4edf-9167-fc0e7759ffa1 · outbound

This paper cites Associa- tion for Computing Machinery.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning Associa- tion for Computing Machinery

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T08:31:07.068133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation af944b10-89c8-4112-934b-abec1ef0aeea · outbound

This paper cites Federated Optimization in Heterogeneous Networks.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning Federated Optimization in Heterogeneous Networks

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T08:31:06.792772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation d1ce8cf5-3de1-4700-8668-5a493132cd7e · outbound

This paper cites Qwen2.5 Technical Report.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning Qwen2.5 Technical Report

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T08:31:06.782596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ca296127-2a0c-49e1-97e6-99c06bb300a1 · outbound

This paper cites Training Production Language Models without Memorizing User Data.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning Training Production Language Models without Memorizing User Data

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-18T08:31:06.787860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation d8fb95cc-fd2c-4e25-b4f0-a2022318639e · outbound

This paper cites InLecture Notes in Computer Science, volume 9283, pages 402–413.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning InLecture Notes in Computer Science, volume 9283, pages 402–413

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T08:31:07.072227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a4fafa6d-064c-4ae9-8d8e-30e607dd6bb6 · outbound

This paper cites InICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning InICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T08:31:07.089786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5dbbe681-7d9f-4f91-a724-0761878df6ab · outbound

This paper cites an unresolved cited work.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-05-18T08:31:07.096514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a105324d-a4cc-4e2a-a3fd-02f952878d8f · outbound

This paper cites The dataset contains abstracts collected from arXiv.org.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning The dataset contains abstracts collected from arXiv.org

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T08:31:07.075678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c7676d4a-8b42-4411-a8fc-088be8463e55 · outbound

This paper cites It con- tains around 200,000 abstracts from random- ized controlled trials.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning It con- tains around 200,000 abstracts from random- ized controlled trials

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T08:31:07.079321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7c351797-4259-498a-9212-5abcea0d0343 · outbound

This paper cites (2023), we empirically study multiple decoding methods: top-k, top-p, and temperature.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning (2023), we empirically study multiple decoding methods: top-k, top-p, and temperature

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T08:31:07.085894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 235ade6a-a803-44b9-b46a-e55339adb6da · outbound

This paper cites an unresolved cited work.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-05-18T08:31:07.093106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c43e8eb5-f6e7-4c6f-a5fe-5fcdc37c74db · outbound

This paper cites E.4 Hyperparameters for Memorization Measurement Following Zeng et al.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning E.4 Hyperparameters for Memorization Measurement Following Zeng et al

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T08:31:07.103301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b779dad4-d797-4a49-9670-d49b615054a2 · outbound

This paper cites Specifically, in summarization and classification tasks, the median of generated suffix lengths is close to the output length.

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning Specifically, in summarization and classification tasks, the median of generated suffix lengths is close to the output length

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T08:31:07.099854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Pith citing papers

Observation 9a1b4723-6c40-4a1a-8d1f-3d9dbfb3361b · inbound

Extracting Training Data from Diffusion Language Models via Infilling cites this paper.

Extracting Training Data from Diffusion Language Models via Infilling Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-06-30T16:04:53.239559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.