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Paper Citation Record · LEDGER

FedAdapter: Efficient Federated Learning for Modern NLP

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2205.10162.

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

pith.paper-citation-record.v1
2205.10162 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:20:46.186692Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T17:08:12.542881Z

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 245ee8b5-da37-44c5-93b5-896ecec8852f · inbound

Federated Co-tuning Framework for Large and Small Language Models cites this paper.

Federated Co-tuning Framework for Large and Small Language Models FedAdapter: Efficient Federated Learning for Modern NLP

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-23T17:08:12.544845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-23T17:08:05.240432Z digest=sha256:646677dd6793a8b15ca7a800a82f22cf7b61a31bf7c2e97a2d29ba7b1b0a08e9

Observation 3cc42069-44e4-4dab-b979-d1cc4cae243a · inbound

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption cites this paper.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption FedAdapter: Efficient Federated Learning for Modern NLP

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T12:20:46.186692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:20:46.186692Z digest=sha256:d60ecf927f9b8c60541daa63c2b2138d61accd731e7b7b07d7f7b59cfc66255f

Observation a491e300-2bd8-441a-bf3d-bb94fca54438 · inbound

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning cites this paper.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning FedAdapter: Efficient Federated Learning for Modern NLP

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T11:57:57.393881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:57:57.393881Z digest=sha256:31bb281beee7f37e691586306a90fecad861924a6c2ec89a82c87a842def0fe0

Observation 89a7bf9b-f8fd-4e85-8206-4f94cdac08d7 · inbound

Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning cites this paper.

Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning FedAdapter: Efficient Federated Learning for Modern NLP

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T10:20:41.314544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:20:41.314544Z digest=sha256:92bc2ce860b57a5ed25da35ae52dd5404b27addf8953be09d74aa3aca83e019d

Observation 3717be6b-0776-42b3-b5ab-db9df47284fd · inbound

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE cites this paper.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE FedAdapter: Efficient Federated Learning for Modern NLP

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T23:43:10.734745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:43:10.734745Z digest=sha256:2b709f33fd083244f35e807b0b269df28d8470f58699a242f79c634da5a914ec

Observation 2075ca81-afd5-42e3-9c69-c53b37d86125 · inbound

Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning cites this paper.

Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning FedAdapter: Efficient Federated Learning for Modern NLP

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T19:20:55.575261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:20:55.575261Z digest=sha256:9e506b5a275823ebd901f1ca47d0b2f9fcde8000fa5a7e03b7c0eccdabfdef69

Observation a916bef0-00f0-447a-8170-53aac933f0d1 · inbound

FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs cites this paper.

FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs FedAdapter: Efficient Federated Learning for Modern NLP

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T23:30:51.361532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T19:04:41.807582Z digest=sha256:b49cc78e69d0ef5f1dc2a40b9815dce126c49196e24f774b84ca245e0f75c4e0

Observation bf8ed1b3-d059-46ef-b8b2-17f2d8f33d52 · inbound

Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge cites this paper.

Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge FedAdapter: Efficient Federated Learning for Modern NLP

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-11T00:30:51.455670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-10T18:30:14.867025Z digest=sha256:fee392835ac22b83aa32bb1a0a06f7a1e652cf28045c95df509e678a487076dd

Observation ef28c61d-301b-4861-bbe1-759b72c0a03e · 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 FedAdapter: Efficient Federated Learning for Modern NLP

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:56:06.048457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-10T02:35:40.593397Z digest=sha256:4d3e785e5f865f3437639ee92d893162a7a9f6008264429bd2a2a2a2938128c7