Paper Citation Record · LEDGER

FedAdapter: Efficient Federated Learning for Modern NLP

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 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 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:46:32.914527Z

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 2026-08-13T06:32:02.005865+00:00.

Observation 4c023d99-a850-4997-8e0b-d48fd22970a3 · inbound

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices cites this paper.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices FedAdapter: Efficient Federated Learning for Modern NLP

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T23:46:32.914527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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.

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.

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.

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.

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 2026-08-13T06:32:02.005865+00:00.

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 2026-08-13T06:32:02.005865+00:00.

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 2026-08-13T06:32:02.005865+00:00.