Paper Citation Record · LEDGER

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning

As of 17 August 2026, this Paper Citation Record lists 100 of 132 outbound references and 7 inbound Pith citation observations for arXiv:2504.21099.

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

pith.paper-citation-record.v1
2504.21099 v1

Coverage vector

measured 100 of 132 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:16:30.760683Z

measured 107 of 107 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:02:50.150062Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T19:08:54.370557Z

Reference resolution

100 of 132 outbound references displayed

verified exact0
verified fuzzy11
unresolved85
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d3915ca2-46e1-486c-8f6c-529097770a32 · outbound

This paper cites GPT-4 Technical Report.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.865606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e63bd60c-a52f-4733-9153-febcb97b3f42 · outbound

This paper cites In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.872180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16afeb3d-64d1-4775-bf5d-b32e4416c57e · outbound

This paper cites In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.877588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 251663fc-b786-4a4e-853e-326ba0d406f6 · outbound

This paper cites PaLM 2 Technical Report.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning PaLM 2 Technical Report

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.883066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1d00f36-ca41-479c-bc9b-e057a47a8941 · outbound

This paper cites SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.888845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e79fcdef-aec0-4de6-8efa-16dd53e8fa3f · outbound

This paper cites Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.894683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0389a56a-e6ff-480e-8f41-0eae02f6a2b1 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.901446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1389c94f-b5f2-4d06-8a08-46d6f34f80ee · outbound

This paper cites In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (V olume 2: Short Papers).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (V olume 2: Short Papers)

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.906737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6180e221-713b-4290-a348-6942bc69422f · outbound

This paper cites In: Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.912207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 623f5cd4-6db6-4389-ae1f-82d18ac8e643 · outbound

This paper cites arXiv preprint arXiv:2411.14961 (2024).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning arXiv preprint arXiv:2411.14961 (2024)

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.917524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d2f655a-cde3-4ac4-87af-94dc2c5b4c49 · outbound

This paper cites arXiv preprint arXiv:2503.11880 (2025).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning arXiv preprint arXiv:2503.11880 (2025)

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.922451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5943e844-75d0-45fd-89f7-5dbf4e2cd544 · outbound

This paper cites In: European Conference on Computer Vision (2014).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: European Conference on Computer Vision (2014)

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.927612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 795c8459-0a16-4e4f-a980-ef0ebd255dcb · outbound

This paper cites FedAdapter: Efficient Federated Learning for Modern NLP.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning FedAdapter: Efficient Federated Learning for Modern NLP

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.932818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a98d42c3-735f-40c7-9b2a-cfeffc0e3204 · outbound

This paper cites Patterns 5(12) (2024).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Patterns 5(12) (2024)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.938463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3467eec9-1522-4338-a949-e4bcfcd6cd3a · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the AAAI Conference on Artificial Intelligence

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.943626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cda7400e-c832-4aeb-833c-be04e50a4884 · outbound

This paper cites arXiv preprint arXiv:2502.01755 (2025).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning arXiv preprint arXiv:2502.01755 (2025)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.948627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4109baa1-afd4-4244-b57b-9b4a78d123f9 · outbound

This paper cites Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T05:16:31.883516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 961f5990-3854-42a0-a087-6ab22bb95ad8 · outbound

This paper cites See https://vicuna.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning See https://vicuna

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.959409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1aa359e-edaa-4a78-937c-dc61f1101468 · outbound

This paper cites Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.964412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fad79a63-fe45-4206-9025-4732012268b9 · outbound

This paper cites VAuLT: Augmenting the Vision-and-Language Transformer for Sentiment Classification on Social Media.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning VAuLT: Augmenting the Vision-and-Language Transformer for Sentiment Classification on Social Media

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T05:16:31.845315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 56b161df-ed83-420d-be78-10e9fac905f5 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Training Verifiers to Solve Math Word Problems

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.975703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 008512a8-bdf0-4dfe-b8c0-ae46f7a3b4b7 · outbound

This paper cites In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.981132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc7f6a22-ea11-4563-b530-cbec58ec71bf · outbound

This paper cites In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.986405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94e65aa0-0d86-4fe6-a586-40aecddd9b0d · outbound

This paper cites Proceedings of the 2nd International Workshop on Compiler Compilers (1992), https://dl.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Proceedings of the 2nd International Workshop on Compiler Compilers (1992), https://dl

Reference 24

Resolution: metadata mismatch; raw_fallback, observed 2026-08-16T05:16:31.806977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7681ee82-5c08-4cdf-9de1-e07505545df4 · outbound

This paper cites In: Machine learning challenges workshop.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Machine learning challenges workshop

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:29.996505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 092fe09b-6209-4eae-92ab-8570713922cd · outbound

This paper cites Databricks Blog (2023), https://www.databricks.com/blog/2023/04/12/ dolly-first-open-commercially-viable-instruction-tuned-llm.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Databricks Blog (2023), https://www.databricks.com/blog/2023/04/12/ dolly-first-open-commercially-viable-instruction-tuned-llm

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.001426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cfd9dd5-5140-4692-8cfb-5b85a0cff8f3 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.436184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af0865b4-c711-4339-b347-02466d43398d · outbound

This paper cites In: Proceedings of NAACL-HLT.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of NAACL-HLT

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.445778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c06ba28-0f3c-4e2d-a10a-a0a6a4ce1977 · outbound

This paper cites HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.450027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d721e296-59e8-457d-8f6f-c916896f475f · outbound

This paper cites In: International Conference on Learning Representations (ICLR) (2021).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: International Conference on Learning Representations (ICLR) (2021)

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.454037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7c8dbfc-6314-4844-9f0a-2fe55719bf00 · outbound

This paper cites an unresolved cited work.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Unresolved cited work

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.458030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c705e9d-1b0a-4d1f-ae2b-2ad3674894e6 · outbound

This paper cites IEEE/ACM Transactions on Audio, Speech, and Language Processing (2024).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning IEEE/ACM Transactions on Audio, Speech, and Language Processing (2024)

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.462010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13f817f2-fd5e-4c85-b770-49d693ea5d5f · outbound

This paper cites AI 5(4), 2773–2800 (2024).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning AI 5(4), 2773–2800 (2024)

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.466201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c143acd6-b942-41f8-bfb3-4c0d8826a3a9 · outbound

This paper cites an unresolved cited work.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Unresolved cited work

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.470275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d32b941-8d0e-45ec-b882-74c881ce5421 · outbound

This paper cites CS224N project report, Stanford 1(12), 2009 (2009).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning CS224N project report, Stanford 1(12), 2009 (2009)

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.474159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d322f152-6002-4ada-8607-b41f308682ab · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.478055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bac3c85d-dc3f-497c-85c0-385bbb22dd62 · outbound

This paper cites In: Proceedings of the ACM Web Conference 2024.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the ACM Web Conference 2024

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.482047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c339316d-c40c-4d7d-b073-37dc54d0ae9e · outbound

This paper cites Parameter-Efficient Transfer Learning with Diff Pruning.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Parameter-Efficient Transfer Learning with Diff Pruning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.486191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdffa355-9288-41c2-bccb-292307b3924a · outbound

This paper cites Selective Aggregation for Low-Rank Adaptation in Federated Learning.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Selective Aggregation for Low-Rank Adaptation in Federated Learning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.490327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc9e116b-fc30-4074-8060-11d22d166dbe · outbound

This paper cites In: Proceedings of the ACM Web Conference 2023.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the ACM Web Conference 2023

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.494727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc231d89-d152-4b54-bf9c-9bacf0153515 · outbound

This paper cites IEEE Transactions on Mobile Computing 23(5), 5179–5194 (2023).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning IEEE Transactions on Mobile Computing 23(5), 5179–5194 (2023)

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.498569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da2634fe-679b-4c5c-9497-ba3070a390af · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.502694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49ab6571-2aa1-4a7d-9918-9bedb7b53a27 · outbound

This paper cites Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.507132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc1cf432-1801-4586-a266-960a1c8b31e6 · outbound

This paper cites In: Proceed- ings of the IEEE Conference on Computer Vision and Pattern Recognition (2016).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceed- ings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.511718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7162d748-4d2b-4d26-b3ad-55d153ae3083 · outbound

This paper cites In: Proceedings of the 9th International Conference on Learning Representations (ICLR) (2021).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 9th International Conference on Learning Representations (ICLR) (2021)

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.515831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4eb351b-93b4-4282-9cbe-435b60929746 · outbound

This paper cites In: Proceedings of the 36th International Conference on Machine Learning.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 36th International Conference on Machine Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.520327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 038618f0-5072-40f6-980e-97ef674007c8 · outbound

This paper cites ICLR 1(2), 3 (2022).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning ICLR 1(2), 3 (2022)

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.524495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 646392f2-ce27-4d23-bbfe-d3d91eec0b9e · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.528574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe0873c7-01da-461e-b91e-3f9aea191ce7 · outbound

This paper cites Mistral 7B.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Mistral 7B

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.532786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8c8c70a-50f0-45bb-8141-0ab4f2f1c6a7 · outbound

This paper cites In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (2014).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (2014)

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.537912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4428513-5fd3-41dd-985a-ff13102d3c79 · outbound

This paper cites In: The Eleventh International Conference on Learning Representations (2023).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: The Eleventh International Conference on Learning Representations (2023)

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.542085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 069c0304-4164-4d5d-86ac-f37e78be0bb2 · outbound

This paper cites In: International conference on machine learning.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: International conference on machine learning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.546301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3b1702d-a340-42b5-b18f-b69581a03e65 · outbound

This paper cites In: Findings of the Association for Computational Linguistics: ACL 2023.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Findings of the Association for Computational Linguistics: ACL 2023

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.550552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92001c69-c05c-4d15-94aa-4a0ead19b07a · outbound

This paper cites Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.554435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d022ed1-9582-4567-8125-98b97c149fdc · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning VeRA: Vector-based Random Matrix Adaptation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.558828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c25184ab-8aa2-47ce-944e-6edd452ac33b · outbound

This paper cites an unresolved cited work.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Unresolved cited work

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.562901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9eb0822b-bcca-457c-a006-7f6e2f3af685 · outbound

This paper cites In: Machine learning proceedings 1995, pp.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Machine learning proceedings 1995, pp

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.567147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8e72c72-1ace-4a70-bd3f-9b53a2550052 · outbound

This paper cites arXiv preprint arXiv:2502.04387 (2025).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning arXiv preprint arXiv:2502.04387 (2025)

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.571905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88942efe-568e-4b2c-9f9b-38c96553f92c · outbound

This paper cites In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.576424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7047a9c8-d0db-4d3d-a515-bfca717e56d4 · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.580768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09a6f4aa-f0af-4f66-8a43-71b34fd2667a · outbound

This paper cites In: Proceedings of the IEEE International Conference on Computer Vision (2017).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the IEEE International Conference on Computer Vision (2017)

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.584958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c93551d0-72ff-421c-983a-93c17b58b80f · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.589534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2905eec8-ae4d-48eb-829b-2217396010e4 · outbound

This paper cites Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.594397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e5cd780-33a7-4470-84e4-15d651c557fe · outbound

This paper cites arXiv preprint arXiv:2406.12844 (2024).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning arXiv preprint arXiv:2406.12844 (2024)

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.599785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2739fa7f-969b-4a7d-94cd-c08d78881c26 · outbound

This paper cites In: Advances in Neural Information Processing Systems.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Advances in Neural Information Processing Systems

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.604169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97e1436c-f99f-49ac-a4b1-43e9000a6ce2 · outbound

This paper cites In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.608505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 135d498e-743b-4511-b69e-f9d298d44b98 · outbound

This paper cites In: European Conference on Computer Vision (2014).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: European Conference on Computer Vision (2014)

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.612586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae442088-a855-444a-a3fc-0b854752bf12 · outbound

This paper cites In: Advances in Neural Information Processing Systems.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Advances in Neural Information Processing Systems

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.616814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a423eac-e54e-4db0-b4ee-0d224259487c · outbound

This paper cites an unresolved cited work.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.621405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e39fe9a0-1725-4ee7-8f26-b05be748de03 · outbound

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

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices

Reference 70

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T05:16:31.451318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 364394cb-7a2b-4947-b6eb-e77d6cd47874 · outbound

This paper cites In: Forty-first International Conference on Machine Learning (2024).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Forty-first International Conference on Machine Learning (2024)

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.631425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53e298db-8a64-43c2-a9ce-e34b932465e9 · outbound

This paper cites Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.635477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bba019d-59e0-45c8-b33c-a7078e88909c · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.639547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7c56bdf-0ead-4967-a753-471195fbbec2 · outbound

This paper cites Advances in Neural Information Processing Systems 37, 39409–39433 (2024).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Advances in Neural Information Processing Systems 37, 39409–39433 (2024)

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.644077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97719230-b711-4a19-8393-981a0b4e8d8e · outbound

This paper cites an unresolved cited work.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Unresolved cited work

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.648239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c198a14-319d-48b6-9bbf-373959701937 · outbound

This paper cites MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.652995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e366e8f4-3869-4b5b-acd9-afb80d98e751 · outbound

This paper cites Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.658382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b331ac5-1f28-43f1-900a-ec6e37c607ea · outbound

This paper cites FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.662916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c0d1d54-d7cd-4e63-9f4d-ce825034a278 · outbound

This paper cites In: Artificial intelligence and statistics.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Artificial intelligence and statistics

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.667510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31500c9f-27b6-4dd5-8f0e-501a90f37946 · outbound

This paper cites Pointer Sentinel Mixture Models.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Pointer Sentinel Mixture Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.671726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79bd18bd-52fa-4448-813d-b9f754a45a53 · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.676292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e09aeb16-8e2d-4070-9555-a8390f50906e · outbound

This paper cites an unresolved cited work.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Unresolved cited work

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.680858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 974ca76b-bf21-40ee-8ba5-37684c42cd7b · outbound

This paper cites NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2011).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2011)

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.685171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2db6e18-4b83-4c52-a3c5-a2185abaf0ed · outbound

This paper cites In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.689661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f176023c-27b1-4ed9-ad24-61a78bf1daac · outbound

This paper cites In: Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (2008).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (2008)

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.555632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 39afffd0-1df4-4981-ad40-eeae84fb7426 · outbound

This paper cites Advances in Neural Information Processing Systems 37, 30590–30623 (2024).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Advances in Neural Information Processing Systems 37, 30590–30623 (2024)

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.542268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0a8d03c1-548d-4c19-b286-af472977599e · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.528826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 613740dc-fd96-4a5d-bac6-8f80b49dbfd3 · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.514701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b24895a8-2440-47ac-b055-f17b5f55e667 · outbound

This paper cites In: Forty-first International Conference on Machine Learning (2024).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Forty-first International Conference on Machine Learning (2024)

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.499423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1831168f-d6bc-4b45-b661-2463b4c4b5b4 · outbound

This paper cites In: Proceedings of the International Conference on Machine Learning (2021).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the International Conference on Machine Learning (2021)

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.484783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 92e4722a-9451-4a5d-98ae-6fe2b80f82cd · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.720895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31f5190a-b4b4-4e6c-b698-bdaeee8ab8b3 · outbound

This paper cites an unresolved cited work.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:16:32.469972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0fa6bda2-4155-4448-a019-a2e562192d08 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.730204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca2bd313-4472-4b47-b51d-0d33b9ed8fbb · outbound

This paper cites In: 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.456217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5c711a9b-b2e1-4c50-968d-06a9fe58b26c · outbound

This paper cites FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.739127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d05bd1de-7e9d-4e6e-a566-dfa0af027c9e · outbound

This paper cites In: Proceedings of the 2013 conference on empirical methods in natural language processing.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the 2013 conference on empirical methods in natural language processing

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.441265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 76f071c9-9d51-492a-9181-7b91676993a8 · outbound

This paper cites In: European Conference on Computer Vision.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: European Conference on Computer Vision

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.426853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e67d8a2d-0102-48ae-a42c-59bc573eef28 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: Proceedings of the AAAI Conference on Artificial Intelligence

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.412603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f91dc50e-d812-476d-85f1-18e44d6445bf · outbound

This paper cites In: 2024 IEEE International Confer- ence on Big Data (BigData).

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning In: 2024 IEEE International Confer- ence on Big Data (BigData)

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:16:32.398681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1622fca1-0a87-42d4-ace8-ae796cb84dbb · outbound

This paper cites Improving LoRA in Privacy-preserving Federated Learning.

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Improving LoRA in Privacy-preserving Federated Learning

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-16T05:16:30.760683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 08169a01-2ad3-4d39-83fd-6462c9894b1f · inbound

FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing cites this paper.

FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T22:02:50.150062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 972e8ed4-de66-4cc1-89a8-cfe5dd3865ad · inbound

A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation cites this paper.

A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:32:16.622834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59f4c247-278a-4711-a3f6-5167c8c6ee46 · 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 A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T19:48:46.467551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 792fb8a8-d411-4a0c-9ada-55cab5fec6c2 · inbound

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge cites this paper.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T13:09:38.487561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39f13f13-52c3-4982-b7f6-6102d19d44ba · 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 A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:30:51.445573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cd70e0c6-056d-490d-a86e-0b8daa13a7c7 · inbound

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models cites this paper.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:08:54.372572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9f8342d9-9c7b-475b-9bd3-5103ab37fa07 · inbound

FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images cites this paper.

FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T09:26:23.455157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.