Paper Citation Record · LEDGER

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries

As of 8 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2505.23813.

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

pith.paper-citation-record.v1
2505.23813 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:50.120967Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

17 of 17 outbound references displayed

verified exact0
verified fuzzy11
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8d493b88-b743-4fba-9378-2c1c5dc1cc37 · outbound

This paper cites DP-RTFL: Differentially Private Resilient Temporal Federated Learning Framework,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries DP-RTFL: Differentially Private Resilient Temporal Federated Learning Framework,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:53.403744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8dc6c065-b511-4de5-8efa-3697204091db · outbound

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

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Communication-efficient learning of deep networks from decentralized data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:53.158543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 148feb6c-22ef-4e6e-acb8-a9448a95574c · outbound

This paper cites L119, pp.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries L119, pp

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:52.886460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 90bbb531-dbcb-4d01-a49f-080bffdceb2f · outbound

This paper cites Credit Card Approval Prediction,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Credit Card Approval Prediction,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:52.638119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c8389815-abe2-4505-81b7-221e8df00cec · outbound

This paper cites Advances and open problems in federated learning,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Advances and open problems in federated learning,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:48.790833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e2d07a4-c955-49ec-ab9d-419d3544bd4f · outbound

This paper cites Calibrating noise to sensitivity in private data analysis,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Calibrating noise to sensitivity in private data analysis,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:52.466345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 86458248-b1ca-4ff8-9df6-5f10332ef4a0 · outbound

This paper cites Dwork and A.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Dwork and A

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:52.244741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02d85314-ecd8-4a71-a12a-45012a6fc2e8 · outbound

This paper cites Hybrid alpha- beta-gamma: A differentially private federated learning framework,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Hybrid alpha- beta-gamma: A differentially private federated learning framework,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:52.051454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b9bf5cda-ffd4-449f-9593-60ff765049bc · outbound

This paper cites Ldp-fl: Practical private aggregation in federated learning with local differential privacy,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Ldp-fl: Practical private aggregation in federated learning with local differential privacy,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:51.764759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 591fae9c-c309-46ec-9fa4-35562ac39c2d · outbound

This paper cites Ma- chine learning with adversaries: Byzantine tolerant gradient descent,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Ma- chine learning with adversaries: Byzantine tolerant gradient descent,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:51.569887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 57740355-6f1f-4ed6-9b7b-3e8338f5b2b2 · outbound

This paper cites Byzantine-robust dis- tributed learning: Towards optimal statistical rates,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Byzantine-robust dis- tributed learning: Towards optimal statistical rates,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:49.411368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9f2b227-2db2-469f-b58d-7813e271385e · outbound

This paper cites Novel magnesium borides and their superconductivity.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Novel magnesium borides and their superconductivity

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T13:35:50.654662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e692ae1-80fb-4592-a3d5-4f9bb5cdfca1 · outbound

This paper cites Jama (jama): Verifi- able function secret sharing for privacy-preserving federated learning,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Jama (jama): Verifi- able function secret sharing for privacy-preserving federated learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:51.366109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4aca8b63-004b-4a35-9c3e-4aaafcaa289b · outbound

This paper cites A digital signature based on a conventional encryption function,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries A digital signature based on a conventional encryption function,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:51.152537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fb8f2db8-dfe3-4f84-bd28-ca73d0e827bf · outbound

This paper cites Sketching phase diagrams using low-depth variational quantum algorithms.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Sketching phase diagrams using low-depth variational quantum algorithms

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T13:35:50.370042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5688afc2-0c0d-425d-9f99-68e2259def1a · outbound

This paper cites Anomaly detection: A survey,.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Anomaly detection: A survey,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:49.953466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19bfb7e9-cf07-4ed2-89a0-5126020ca57e · outbound

This paper cites an unresolved cited work.

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:35:50.905469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.