Paper Citation Record · LEDGER

A Survey on Efficient Federated Learning Methods for Foundation Model Training

As of 5 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 2 inbound Pith citation observations for arXiv:2401.04472.

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

pith.paper-citation-record.v1
2401.04472 v3

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T04:43:50.917201Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:37:33.928413Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-22T15:34:57.715979Z

Reference resolution

73 of 73 outbound references displayed

verified exact1
verified fuzzy71
unresolved1
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c8c7dc7c-5eb5-4bd7-97ed-949254c5af6c · outbound

This paper cites Parizi, and Fahad Saeed.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Parizi, and Fahad Saeed

Reference 1

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 94a5695a-6d5c-45e1-be3d-ea3c4e848370 · outbound

This paper cites Qsgd: Communication-efficient sgd via gradient quantization and encoding.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Qsgd: Communication-efficient sgd via gradient quantization and encoding

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.292616Z

Source-reported events for the cited work

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Observation e1cefd98-9b6b-4b29-823c-0ce047fdffaf · outbound

This paper cites Elkordy, et al.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Elkordy, et al

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.273461Z

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Observation 5c59a68b-2e8b-4110-8d7c-706e6994c0d2 · outbound

This paper cites Federated learning review: Fundamentals, enabling technologies, and future applications.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Federated learning review: Fundamentals, enabling technologies, and future applications

Reference 4

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Observation 0e76d591-fbd5-421e-9f8d-2c0dc1f24cdf · outbound

This paper cites Beutel, Taner Topal, et al.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Beutel, Taner Topal, et al

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.375074Z

Source-reported events for the cited work

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Observation 393bc9e5-5d20-429d-938c-5a8753adbea0 · outbound

This paper cites Hudson, et al.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Hudson, et al

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.177684Z

Source-reported events for the cited work

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Observation a2cf3e9b-9fe6-4fab-a6b7-4f11cfa1aa63 · outbound

This paper cites Fedobd: Opportunistic block dropout for efficiently training large-scale neural networks through federated learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Fedobd: Opportunistic block dropout for efficiently training large-scale neural networks through federated learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.378412Z

Source-reported events for the cited work

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Observation 527b3d7d-78f1-4b8c-88ab-3b227b525722 · outbound

This paper cites Heterofl: Computation and communication efficient federated learning for heterogeneous clients.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Heterofl: Computation and communication efficient federated learning for heterogeneous clients

Reference 8

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Observation ac176d1c-22e2-4f03-9b9b-821b3efccd4e · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Parameter-efficient fine-tuning of large-scale pre-trained language models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.205018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 609d743e-c42f-40b0-8576-63bd75ac967e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

A Survey on Efficient Federated Learning Methods for Foundation Model Training An image is worth 16x16 words: Transformers for image recognition at scale

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.343123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4aca854c-f129-4a1c-a2b9-e87b5287823a · outbound

This paper cites How can we train deep learning models across clouds and continents? an experimental study.

A Survey on Efficient Federated Learning Methods for Foundation Model Training How can we train deep learning models across clouds and continents? an experimental study

Reference 11

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Observation b04e32d4-6a8f-42b8-977e-626bd17eef32 · outbound

This paper cites Fate-llm: A industrial grade federated learning framework for large language models.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Fate-llm: A industrial grade federated learning framework for large language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.135784Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation de5c2a5f-8042-46fa-abe6-381621b685d4 · outbound

This paper cites Openfl: the open federated learning library.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Openfl: the open federated learning library

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.317980Z

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Observation b7328150-136d-4db5-ad0f-646c59d5ebe6 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks.

A Survey on Efficient Federated Learning Methods for Foundation Model Training The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.181617Z

Source-reported events for the cited work

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Observation df910ff8-55c3-4760-bb87-7bb059996a42 · outbound

This paper cites Substra: a framework for privacy-preserving, traceable and collaborative machine learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Substra: a framework for privacy-preserving, traceable and collaborative machine learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.189539Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6f5a9f1c-3b05-46a9-82be-73b07953a50b · outbound

This paper cites an unresolved cited work.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Unresolved cited work

Reference 16

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Observation ce514983-e9b4-4160-acb2-8aba2755ce02 · outbound

This paper cites Knowledge distillation in vision transformers: A critical review.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Knowledge distillation in vision transformers: A critical review

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.214133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9b176dc1-05eb-43b7-8e50-0d4631a9749e · outbound

This paper cites Fedml: A research library and benchmark for federated machine learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Fedml: A research library and benchmark for federated machine learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.163986Z

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Observation c217e7c2-ea27-4157-be1b-ceb1df68e29b · outbound

This paper cites Distiller: A systematic study of model distillation methods in natural language processing.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Distiller: A systematic study of model distillation methods in natural language processing

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.287608Z

Source-reported events for the cited work

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Observation 9abc0839-0742-4766-94b7-6335d6d0fd67 · outbound

This paper cites Fj ORD : Fair and accurate federated learning under heterogeneous targets with ordered dropout.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Fj ORD : Fair and accurate federated learning under heterogeneous targets with ordered dropout

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.156459Z

Source-reported events for the cited work

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Observation df40b093-5cd0-472a-af7e-786845e4bf6f · outbound

This paper cites Parameter-efficient transfer learning for nlp.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Parameter-efficient transfer learning for nlp

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.268415Z

Source-reported events for the cited work

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Observation 0d86940a-7432-4a60-ac54-d74af12325b0 · outbound

This paper cites Hu, Yelong Shen, et al.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Hu, Yelong Shen, et al

Reference 22

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Observation 18711d48-3a8c-44e6-a7ed-15f78a81bf86 · outbound

This paper cites Distributed pruning towards tiny neural networks in federated learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Distributed pruning towards tiny neural networks in federated learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.091604Z

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Observation aace2891-6510-46b9-8f1d-aa6038d36383 · outbound

This paper cites Sparse random networks for communication-efficient federated learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Sparse random networks for communication-efficient federated learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.371785Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7b64c6ff-b648-4685-aaa0-54102691e61e · outbound

This paper cites Visual prompt tuning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Visual prompt tuning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.160217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ecbba3b6-e181-4af6-ab80-d114b0f8ef8b · outbound

This paper cites Model pruning enables efficient federated learning on edge devices.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Model pruning enables efficient federated learning on edge devices

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.237779Z

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Observation d8e5077f-b66f-4c5f-9213-97baa7eb7e33 · outbound

This paper cites Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.383336Z

Source-reported events for the cited work

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Observation a6dc369b-563d-4dbd-9d8e-e93b41aff22e · outbound

This paper cites Block pruning for faster transformers.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Block pruning for faster transformers

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.310338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2f645b90-abed-4e02-9204-88b5ce38aec7 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training The power of scale for parameter-efficient prompt tuning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.125810Z

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Observation 8ad153bd-a158-4b6b-93db-ccb0df48503d · outbound

This paper cites Lotteryfl: Personalized and communication-efficient federated learning with lottery ticket hypothesis on non-iid datasets.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Lotteryfl: Personalized and communication-efficient federated learning with lottery ticket hypothesis on non-iid datasets

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.222198Z

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Observation a08850a1-ff7c-4273-b9a4-84e3913fa9a8 · outbound

This paper cites Soteriafl: A unified framework for private federated learning with communication compression.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Soteriafl: A unified framework for private federated learning with communication compression

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.118738Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4c2c0218-3de1-4733-9384-dd48a8d12572 · outbound

This paper cites A survey on federated learning systems: Vision, hype and reality for data privacy and protection.

A Survey on Efficient Federated Learning Methods for Foundation Model Training A survey on federated learning systems: Vision, hype and reality for data privacy and protection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.147951Z

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Observation 7b6f533c-1651-411f-bc66-17121c9a086a · outbound

This paper cites Fate: An industrial grade platform for collaborative learning with data protection.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Fate: An industrial grade platform for collaborative learning with data protection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.298361Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cd134a07-74f6-487c-ad42-381903eb4481 · outbound

This paper cites From distributed machine learning to federated learning: a survey.

A Survey on Efficient Federated Learning Methods for Foundation Model Training From distributed machine learning to federated learning: a survey

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.368053Z

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Observation 4c465ec3-0a83-4c65-bfe0-961e167f6de2 · outbound

This paper cites The flan collection: Designing data and methods for effective instruction tuning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training The flan collection: Designing data and methods for effective instruction tuning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.393574Z

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Observation bd323628-6f39-45e0-ac22-309223a33da4 · outbound

This paper cites Fedclip: Fast generalization and personalization for clip in federated learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Fedclip: Fast generalization and personalization for clip in federated learning

Reference 36

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Source-reported events for the cited work

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Observation 2307f0f3-eb1e-44d9-a88b-2fe4cc93cccc · outbound

This paper cites Ibm federated learning: an enterprise framework white paper v0.1.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Ibm federated learning: an enterprise framework white paper v0.1

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.233632Z

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Observation 563b5319-8468-4a03-9beb-bc0e9967da7d · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Communication-Efficient Learning of Deep Networks from Decentralized Data

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.241563Z

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Observation ebf0f4bb-ec47-436c-916b-9ab83a29c4b7 · outbound

This paper cites Distributed learning with compressed gradient differences.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Distributed learning with compressed gradient differences

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.351075Z

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Observation 7bb7f161-d3a7-4e6c-839a-d85158129ad1 · outbound

This paper cites Pappas, and Hamed Hassani.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Pappas, and Hamed Hassani

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.226160Z

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Observation 3a4525a1-e6e3-4449-ab4e-b82869a0231d · outbound

This paper cites Nguyen, Ming Ding, et al.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Nguyen, Ming Ding, et al

Reference 41

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Observation 83c97df0-52f4-439b-a200-f4307ad0ee3e · outbound

This paper cites GPT-4 technical report.

A Survey on Efficient Federated Learning Methods for Foundation Model Training GPT-4 technical report

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.111472Z

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Observation 65edf5d4-7996-4def-93d7-6ab418c21144 · outbound

This paper cites The refinedweb dataset for falcon llm: Outperforming curated corpora with web data, and web data only.

A Survey on Efficient Federated Learning Methods for Foundation Model Training The refinedweb dataset for falcon llm: Outperforming curated corpora with web data, and web data only

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.326370Z

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Observation 95c89b31-719b-44c7-9a0a-e048d4c981e8 · outbound

This paper cites Federated adversarial domain adaptation.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Federated adversarial domain adaptation

Reference 44

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Observation 3bf2cea9-94fb-4e01-88bd-9022b5a2c24f · outbound

This paper cites Audio-visual model distillation using acoustic images.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Audio-visual model distillation using acoustic images

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.305471Z

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Observation b7953d01-32ea-410c-a056-5e09b5bc6796 · outbound

This paper cites Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.354936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3d2ef639-18cb-4669-932a-75e35c365748 · outbound

This paper cites Language models are unsupervised multitask learners.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Language models are unsupervised multitask learners

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.346987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d7df0b31-7099-48f2-b25a-fd8f82376152 · outbound

This paper cites Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.257590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f4725283-d572-4a40-aae9-6e305f4ce1a9 · outbound

This paper cites Roth, Yan Cheng, et al.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Roth, Yan Cheng, et al

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.249450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 92cc6dcd-7e07-4dab-8335-6c06cf252cbd · outbound

This paper cites Swarm parallelism: Training large models can be surprisingly communication-efficient.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Swarm parallelism: Training large models can be surprisingly communication-efficient

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.129725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b44bfe31-d8cd-4c07-a48e-86d9f4b11c8e · outbound

This paper cites Movement pruning: Adaptive sparsity by fine-tuning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Movement pruning: Adaptive sparsity by fine-tuning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.338884Z

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Observation 5226bfc2-8ea8-43da-9637-8e9013d8d5a2 · outbound

This paper cites Exploring parameter-efficient fine-tuning for improving communication efficiency in federated learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Exploring parameter-efficient fine-tuning for improving communication efficiency in federated learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.115033Z

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Observation 88ee594a-9155-45d3-8dd3-4e63f7ab35a0 · outbound

This paper cites Stanford alpaca: An instruction-following llama model.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Stanford alpaca: An instruction-following llama model

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.193664Z

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Observation 8ed953fc-bd7c-4d41-b872-310ee72ec141 · outbound

This paper cites Fedbert: When federated learning meets pre-training.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Fedbert: When federated learning meets pre-training

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.253186Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4c2d2d8c-998a-4790-b0fc-6e5e30beb558 · outbound

This paper cites Federated fine-tuning of llms on the very edge: The good, the bad, the ugly.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Federated fine-tuning of llms on the very edge: The good, the bad, the ugly

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.152460Z

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Observation 74e9149a-da30-4038-b867-f6be64fb3e5e · outbound

This paper cites Federatedscope: A flexible federated learning platform for heterogeneity.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Federatedscope: A flexible federated learning platform for heterogeneity

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.173937Z

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Observation cceb8698-886b-4ee8-b259-39e2b7115e15 · outbound

This paper cites Federated learning of gboard language models with differential privacy.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Federated learning of gboard language models with differential privacy

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.099879Z

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Observation 5c1b3cc6-fd26-4679-bf4a-f7896085a0d4 · outbound

This paper cites Uniaudio: An audio foundation model toward universal audio generation.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Uniaudio: An audio foundation model toward universal audio generation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.387490Z

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Observation ac3df5ef-445a-4a96-86d3-02654dd795f5 · outbound

This paper cites Dual-Personalizing Adapter for Federated Foundation Models.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Dual-Personalizing Adapter for Federated Foundation Models

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-24T04:43:53.725478Z

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Observation bd1b658d-6c47-4623-8ba3-b82c9f94ecf0 · outbound

This paper cites Green federated learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Green federated learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.169484Z

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Observation deffff3a-f1c1-417c-8b6c-29fd55112f6d · outbound

This paper cites Pablo Muñoz, and Ali Jannesari.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Pablo Muñoz, and Ali Jannesari

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.282978Z

Source-reported events for the cited work

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Observation d80952bf-79b1-43af-97ca-534e99db6432 · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.245930Z

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Observation 46e2739d-34bf-406d-82fe-88f85c730db5 · outbound

This paper cites A survey on federated learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training A survey on federated learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.278820Z

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Observation 200e6aac-ba09-44ff-b825-63cf43d5c107 · outbound

This paper cites Towards building the federated gpt: Federated instruction tuning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Towards building the federated gpt: Federated instruction tuning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.363943Z

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Observation 3cc8187b-f3be-4c4b-a29d-6e425c10df1a · outbound

This paper cites FedPETuning : When federated learning meets the parameter-efficient tuning methods of pre-trained language models.

A Survey on Efficient Federated Learning Methods for Foundation Model Training FedPETuning : When federated learning meets the parameter-efficient tuning methods of pre-trained language models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.201356Z

Source-reported events for the cited work

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Observation a40f9308-01e9-45d7-8a34-0e0948f7802b · outbound

This paper cites Fedprompt: Communication-efficient and privacy preserving prompt tuning in federated learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Fedprompt: Communication-efficient and privacy preserving prompt tuning in federated learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.330837Z

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Observation 8f51e11d-e484-45f7-a72d-d4a4dadf6942 · outbound

This paper cites Secrets of rlhf in large language models part i: Ppo.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Secrets of rlhf in large language models part i: Ppo

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.230025Z

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Observation 1e14532c-f2f0-46d1-99aa-be6db4053911 · outbound

This paper cites Adaptive quantization for deep neural network.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Adaptive quantization for deep neural network

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.103557Z

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Observation 59c68b44-985c-4974-a423-be9f748e5792 · outbound

This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

A Survey on Efficient Federated Learning Methods for Foundation Model Training To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.122498Z

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Observation ca3faecf-0b94-40f9-98cf-3c99103a9289 · outbound

This paper cites Sparse tensor core: Algorithm and hardware co-design for vector-wise sparse neural networks on modern gpus.

A Survey on Efficient Federated Learning Methods for Foundation Model Training Sparse tensor core: Algorithm and hardware co-design for vector-wise sparse neural networks on modern gpus

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.359042Z

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Observation 46a76271-99cd-4a03-936c-c81ca96dbf78 · outbound

This paper cites When foundation model meets federated learning: Motivations, challenges, and future directions.

A Survey on Efficient Federated Learning Methods for Foundation Model Training When foundation model meets federated learning: Motivations, challenges, and future directions

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.314218Z

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Observation 32081aab-fd54-4d69-acaa-873aeb88671e · outbound

This paper cites PySyft: A Library for Easy Federated Learning.

A Survey on Efficient Federated Learning Methods for Foundation Model Training PySyft: A Library for Easy Federated Learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.106790Z

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Observation 03a55b88-eb38-4669-a0a6-192a20c174ab · outbound

This paper cites write newline.

A Survey on Efficient Federated Learning Methods for Foundation Model Training write newline

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:43:54.144023Z

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Pith citing papers

Observation 8acd65e3-f85b-47f4-a856-a1488bff5211 · inbound

A Survey on Foundation Models for Personalized Federated Intelligence cites this paper.

A Survey on Foundation Models for Personalized Federated Intelligence A Survey on Efficient Federated Learning Methods for Foundation Model Training

Reference 183

Resolution: verified exact; local_arxiv, observed 2026-05-22T15:34:57.718057Z

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Observation 5632b368-3823-42a3-9e93-a35ce805544d · inbound

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights cites this paper.

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights A Survey on Efficient Federated Learning Methods for Foundation Model Training

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T05:37:33.928413Z

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