Paper Citation Record · LEDGER

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

As of 22 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 2026-08-22T06:32:14.747728+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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

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

Reference 28

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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

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

Reference 30

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

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

Reference 31

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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

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

Reference 33

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

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

Reference 34

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

Reference 35

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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

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

Reference 37

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

Reference 38

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

Reference 39

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

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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

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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

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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

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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)

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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

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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

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

Reference 47

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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

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

Reference 49

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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

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

Reference 51

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

Reference 52

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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

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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

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

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

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

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

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

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

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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

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Reference 80

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

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

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

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

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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

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

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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

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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

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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

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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

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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

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