Paper Citation Record · LEDGER

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions

As of 12 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 5 inbound Pith citation observations for arXiv:2501.04436.

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

pith.paper-citation-record.v1
2501.04436 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:36:09.204976Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:59:20.828982Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:56:14.197253Z

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy8
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2b7610ba-b6bb-4860-b776-dd3f94737f90 · outbound

This paper cites A survey on evaluation of large language models,.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions A survey on evaluation of large language models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:36:09.389904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0cf77eaa-baaf-48a9-a182-ccb70c830443 · outbound

This paper cites Scaling federated learning for fine-tuning of large language models,.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions Scaling federated learning for fine-tuning of large language models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:36:09.378424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 62fc1ea7-bc80-495b-802a-f8dba2f65c5c · outbound

This paper cites Language models are few-shot learners,.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions Language models are few-shot learners,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:36:09.365577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7a389737-4e14-469d-a26f-750ba3192dd2 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:36:09.351765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9019eee1-f4a3-47cb-a50a-fcbfc58fd0ac · outbound

This paper cites When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T21:36:09.168255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ae64b24-ae80-4b8f-86da-4d10807eb6e0 · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models,.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions Parameter-efficient fine-tuning of large-scale pre-trained language models,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:36:09.338461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c2badd44-49b7-45f7-a9be-2fb22cc7cac7 · outbound

This paper cites FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:36:09.180084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bca5fa91-3e4f-4931-aa33-76421a073c0d · outbound

This paper cites SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T21:36:09.185354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9501f02a-2a1f-4d74-9ca0-b92e22c66fca · outbound

This paper cites Communication-efficient federated learning via knowledge distillation,.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions Communication-efficient federated learning via knowledge distillation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:36:09.325214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f42b568c-d29d-468c-9f6a-4dd4756d1c4e · outbound

This paper cites Splitfed: When federated learning meets split learning,.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions Splitfed: When federated learning meets split learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:36:09.311964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a9e3fb38-da32-462c-8d35-cba2cd1dc0f1 · outbound

This paper cites Language models are unsuper- vised multitask learners,.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions Language models are unsuper- vised multitask learners,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:36:09.298183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c6a5e9ea-b117-4d11-88e3-b988c91d3682 · outbound

This paper cites Efficient Intent Detection with Dual Sentence Encoders.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions Efficient Intent Detection with Dual Sentence Encoders

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T21:36:09.204976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation cfcfadf3-390f-4018-b498-fdd03ca1a5c8 · inbound

Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management cites this paper.

Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:59:20.828982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a3dad00-74d9-4438-be1d-6003eaef8f6a · inbound

Prompting Wireless Networks: Reinforced In-Context Learning for Power Control cites this paper.

Prompting Wireless Networks: Reinforced In-Context Learning for Power Control Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:59:03.726140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06de215f-6f6d-417b-a95b-0f990b3beeff · inbound

Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks cites this paper.

Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T12:17:48.778685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2307bc8a-fde6-4c0e-8964-b233da2289d3 · inbound

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models cites this paper.

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T19:48:46.857552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 982cef3f-6e2e-4490-a0c1-402706656b59 · inbound

Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support cites this paper.

Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions

Reference 117

Resolution: verified exact; arxiv_id, observed 2026-07-01T20:56:14.198755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.