Paper Citation Record · LEDGER

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment

As of 21 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2506.23358.

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

pith.paper-citation-record.v1
2506.23358 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:51:48.219112Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:00:16.964880Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T15:00:17.185434Z

Reference resolution

37 of 37 outbound references displayed

verified exact1
verified fuzzy27
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 023faeca-d61c-4f31-8a63-c16261e0a829 · outbound

This paper cites Synthesizing electronic health records using improved generative adversarial networks.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Synthesizing electronic health records using improved generative adversarial networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:54.973218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d5719f2c-93c3-48a6-812a-c04794a50017 · outbound

This paper cites FedSyn: Synthetic Data Generation using Federated Learning.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment FedSyn: Synthetic Data Generation using Federated Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:44.973235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac066962-112e-4832-bf4a-877b27ac86e6 · outbound

This paper cites Generating multi-label discrete patient records using generative adversarial networks.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Generating multi-label discrete patient records using generative adversarial networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:54.787796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 37102573-8211-420b-a5cd-c886d46d5398 · outbound

This paper cites Survey of medical applications of federated learning.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Survey of medical applications of federated learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:54.612008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation afd29b98-0e0d-4ac7-966c-d547ed9648ee · outbound

This paper cites Clinicalbert: Modeling clinical notes and predicting hospital readmission.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Clinicalbert: Modeling clinical notes and predicting hospital readmission

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:54.430525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6ddb0733-acb3-453d-8087-eb39e5fb1306 · outbound

This paper cites Emerging trends in federated learning: From model fusion to federated x learning.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Emerging trends in federated learning: From model fusion to federated x learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:54.237280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ad331431-1194-4d93-8bb2-1d24756a2ef8 · outbound

This paper cites Mimic-iv, a freely accessible electronic health record dataset.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Mimic-iv, a freely accessible electronic health record dataset

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:45.473278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d63c1c3c-b357-4cd0-92c1-91bb81aef32e · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learning.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Scaffold: Stochastic controlled averaging for federated learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:54.012778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 133ac8c2-bfa8-4c35-8465-3cb2d6a4ca3a · outbound

This paper cites Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:53.788704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5d006024-4f26-40ff-b23e-f883b499ac0b · outbound

This paper cites Biobert: a pre-trained biomedical language representation model for biomedical text mining.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Biobert: a pre-trained biomedical language representation model for biomedical text mining

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:53.560963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d0b5ef0b-9e99-4fcc-b10d-810fe96e8a32 · outbound

This paper cites Federated optimization in heterogeneous networks.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Federated optimization in heterogeneous networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:45.744632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89fee5c5-ac71-4268-9fd2-462f6f9b2db7 · outbound

This paper cites Behrt: transformer for electronic health records.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Behrt: transformer for electronic health records

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:53.420144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2e91c04c-7eb9-4a18-9061-39c35259f532 · outbound

This paper cites Trading off scalability, privacy, and performance in data synthesis.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Trading off scalability, privacy, and performance in data synthesis

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:53.132468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3e21bacf-2ee1-4133-9057-0aa937e14798 · outbound

This paper cites Federated learning for generating synthetic data: a scoping review.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Federated learning for generating synthetic data: a scoping review

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:52.943122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7014dc7a-b166-4a41-900a-5b24811dd26d · outbound

This paper cites Recent advances on federated learning: A systematic survey.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Recent advances on federated learning: A systematic survey

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:52.744731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0cb041ff-0795-4dd8-a766-ec87333be910 · outbound

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

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Communication-efficient learning of deep networks from decentralized data

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:46.155152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aae0f08a-f954-44f5-bdfc-9dec5ae95bbc · outbound

This paper cites The eicu collaborative research database, a freely available multi-center database for critical care research.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment The eicu collaborative research database, a freely available multi-center database for critical care research

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:52.548976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation addb9f55-1957-4394-bafa-e2c315e0fde0 · outbound

This paper cites How deep is your guess? a fresh perspective on deep learning for medical time-series imputation.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment How deep is your guess? a fresh perspective on deep learning for medical time-series imputation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:52.255938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8f815a2a-fdb7-46db-a3aa-9f9bf18547d0 · outbound

This paper cites Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:46.353075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 667bcea7-ccca-4186-9a73-5275686d40da · outbound

This paper cites Zero shot health trajectory prediction using transformer.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Zero shot health trajectory prediction using transformer

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:52.045225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8b421803-07fc-43a2-9046-782fee48fe0b · outbound

This paper cites MOTOR: A Time-To-Event Foundation Model For Structured Medical Records.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment MOTOR: A Time-To-Event Foundation Model For Structured Medical Records

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:46.536956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e58e900-ea87-43b5-a887-52574f75e188 · outbound

This paper cites Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:51.829404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6441f062-99e2-4002-ab4b-860cc1801570 · outbound

This paper cites an unresolved cited work.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:51:51.582033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 199720d4-24b9-44aa-a9cb-4008cc022b13 · outbound

This paper cites Differentially private synthetic medical data generation using convolutional gans.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Differentially private synthetic medical data generation using convolutional gans

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:51.323111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 738d7c7b-acee-4c6e-b1ba-7a6e577a7315 · outbound

This paper cites Attention is all you need.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Attention is all you need

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:51.087051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d6fe6e01-a35e-46a1-bf7e-281f83df1ee3 · outbound

This paper cites an unresolved cited work.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:51:50.822752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d2439cf4-27e2-402b-b489-bb2f912e05d3 · outbound

This paper cites Generation of Synthetic Electronic Health Records Using a Federated GAN.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Generation of Synthetic Electronic Health Records Using a Federated GAN

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:51:48.438541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 85cecb9c-9ba3-4b36-93a7-716a63730b8f · outbound

This paper cites The shaky foundations of large language models and foundation models for electronic health records.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment The shaky foundations of large language models and foundation models for electronic health records

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:50.566545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 84e58613-a04f-4e17-be6a-4fd3a752079d · outbound

This paper cites A large language model for electronic health records.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment A large language model for electronic health records

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:50.346230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5a253396-0bc3-4157-be13-d5db43650d21 · outbound

This paper cites Ehr-safe: generating high-fidelity and privacy-preserving synthetic electronic health records.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Ehr-safe: generating high-fidelity and privacy-preserving synthetic electronic health records

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:50.131895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3d29b6ff-f89e-4311-bd25-7eeee47949b5 · outbound

This paper cites Federated learning: Overview, strategies, applications, tools and future directions.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Federated learning: Overview, strategies, applications, tools and future directions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:49.892898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4805b9c7-0a98-4f11-b671-983bcb8c7937 · outbound

This paper cites Generating Clinically Realistic EHR Data via a Hierarchy- and Semantics-Guided Transformer.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Generating Clinically Realistic EHR Data via a Hierarchy- and Semantics-Guided Transformer

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:47.564582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f2950cc-35bd-4fc0-9e2e-ff784cef09f3 · outbound

This paper cites The prediction is made based on the entire available patient history up to the point of admission.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment The prediction is made based on the entire available patient history up to the point of admission

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:49.685320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3c98e5e4-6114-45f2-897c-36fc9469e4d0 · outbound

This paper cites The model regresses the score based on historical clinical data up to the time of assessment.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment The model regresses the score based on historical clinical data up to the time of assessment

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:49.394238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9ae44f1c-9fb9-4cbc-b5f1-f48d52c1cf5c · outbound

This paper cites The generation starts from the last token indicating hospital discharge and continues forward in time.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment The generation starts from the last token indicating hospital discharge and continues forward in time

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:49.138744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 08a89139-e11b-40a0-9a65-e4dc92f854e2 · outbound

This paper cites Generation begins from the last token corresponding to hospital admission.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Generation begins from the last token corresponding to hospital admission

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:48.898460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fde34996-8dbd-4b4c-b58e-066e71cb3b5a · outbound

This paper cites Count”) and the corresponding unique-token count (“N.

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment Count”) and the corresponding unique-token count (“N

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:48.683674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation d5f6098c-9058-4403-a40b-136d204341cc · inbound

Federated generative event models for tokenized electronic health records cites this paper.

Federated generative event models for tokenized electronic health records Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:00:17.189434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.