Paper Citation Record · LEDGER

FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2310.10049.

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

pith.paper-citation-record.v1
2310.10049 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:42:38.682227Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

32
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0e7c1600-419d-49e7-9eec-9fa33688202c · inbound

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions cites this paper.

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 202

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:55:50.686836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ab2504b7-a1cd-4d62-99db-65d8d5b8c40c · inbound

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices cites this paper.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.404733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 31c8631f-bd0b-4a71-8c3e-a5934beca9a7 · inbound

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality cites this paper.

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:38.682227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1c70ac8-1979-448a-8e2a-216eac2e053b · inbound

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation cites this paper.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T00:46:10.034673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f3e1433-f316-49f8-97af-5805650af5de · inbound

LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems cites this paper.

LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-04T17:46:17.306627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd7e4bc1-078d-45cb-a53c-83be2108b3fd · inbound

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion cites this paper.

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 85

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:56:06.081616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ea0461e6-0575-4698-8f9a-88e1411eee5f · inbound

FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning cites this paper.

FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-08T22:29:18.080667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1a916ff1-60e0-482d-ad79-c6ec9126dbcc · inbound

Concordia: Self-Improving Synthetic Tables for Federated LLMs cites this paper.

Concordia: Self-Improving Synthetic Tables for Federated LLMs FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:16:27.629563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation be70d23b-098c-4332-a259-c194ecd13b05 · inbound

Concordia: Self-Improving Synthetic Tables for Federated LLMs cites this paper.

Concordia: Self-Improving Synthetic Tables for Federated LLMs FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:23:47.881745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bf5592d5-51ab-41c6-9bf9-4284094ec277 · inbound

FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation cites this paper.

FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:13:15.174284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7770fb83-7d3e-4efd-b324-6571eb6aa03c · inbound

Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation cites this paper.

Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 75

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:07:27.015144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 541c3c0b-f487-4fa5-bc18-97e7681bbb56 · inbound

TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization cites this paper.

TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-27T00:30:16.302594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a037428f-7df3-43f0-8814-900dfa627f3e · inbound

HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning cites this paper.

HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T04:58:48.985125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad39f12e-ff11-4db8-8854-b6c13b0d031e · inbound

HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning cites this paper.

HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T06:56:09.710506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 376a5538-69b2-47a3-89c1-1c1f7882a0fa · inbound

HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning cites this paper.

HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T04:25:54.078883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b14626a-de8e-451a-912d-51271cc171e1 · inbound

Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients cites this paper.

Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T14:16:08.918611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.