Paper Citation Record · LEDGER

FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2211.08025.

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

pith.paper-citation-record.v1
2211.08025 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:53:06.379998Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T13:54:52.907275Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7b60971e-eed2-490f-ade7-c05fe7a0394e · inbound

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics cites this paper.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T18:53:06.379998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b385b422-3f71-4c8b-993a-dac94b1ba29d · inbound

HeteroTune: Efficient Federated Learning for Large Heterogeneous Models cites this paper.

HeteroTune: Efficient Federated Learning for Large Heterogeneous Models FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:09.197619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff6cd8b9-8433-4500-8d30-89f3b55f0fd2 · inbound

FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning cites this paper.

FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T12:20:30.280526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7aa7c0e3-f89b-467c-bffd-b6ad71e14d3b · inbound

Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation cites this paper.

Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T21:57:15.833799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5aff27dd-f850-4e17-b8d7-81b744396350 · inbound

Trustformer: A Trusted Federated Transformer cites this paper.

Trustformer: A Trusted Federated Transformer FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T18:03:48.481932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6da4d65d-5c10-43bf-9185-a8527996d4d1 · inbound

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning cites this paper.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.910543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5bcb026-c3c3-4c70-950c-75691a8f8a4e · inbound

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection cites this paper.

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:54.371195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5198c946-8f49-44c9-b065-9309e5e477bf · inbound

FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA cites this paper.

FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:57:25.007012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.