DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models | Paper Citation Record

Observation efe100aa-0bf4-4c76-8185-fb6d847cd8a5 · outbound

This paper cites Learning transferable visual models from natural language supervision, 2021.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Learning transferable visual models from natural language supervision, 2021

Reference 1

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Observation 38dd7c12-c0b3-4569-93b5-f5f54e707102 · outbound

This paper cites Gpt-4 technical report, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Gpt-4 technical report, 2024

Reference 2

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Observation 0c90f1c7-1c2c-4480-a67c-d10ba2363d05 · outbound

This paper cites Gpt-3: Its nature, scope, limits, and consequences.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Gpt-3: Its nature, scope, limits, and consequences

Reference 3

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Observation c7dea81e-53c2-48c6-97d5-c1eab1d99de3 · outbound

This paper cites Scaling language models: Methods, analysis & insights from training gopher, 2022.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Scaling language models: Methods, analysis & insights from training gopher, 2022

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:00.145566Z

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Observation 5e0e360c-c9f3-46f1-bd35-2409de39db13 · outbound

This paper cites Rae, Oriol Vinyals, and Laurent Sifre.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Rae, Oriol Vinyals, and Laurent Sifre

Reference 5

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Observation 7bd73bf3-fb93-416d-a7d2-db3706e4ef1d · outbound

This paper cites Skipping computations in multimodal llms, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Skipping computations in multimodal llms, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:59.772999Z

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Observation 32f8dd01-0865-4edc-a01d-43d7674f8afc · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:45.669503Z

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Observation 093b4c2e-9367-447d-8e66-0e029e5c9e72 · outbound

This paper cites Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges.IEEE Communications Surveys & Tutorials, 2023.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges.IEEE Communications Surveys & Tutorials, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:59.559370Z

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Observation 6bfcede1-bbbf-4be5-9059-bbd01b89ca51 · outbound

This paper cites Decentralized federated learning: A survey on security and privacy.IEEE Transactions on Big Data, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Decentralized federated learning: A survey on security and privacy.IEEE Transactions on Big Data, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:59.288886Z

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Observation 221081fd-16f1-41b9-909c-adf80bfbda0a · outbound

This paper cites an unresolved cited work.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:42:59.137255Z

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Observation 3906c495-d8b2-43a7-8a2d-7ab7f898c474 · outbound

This paper cites Decentralized Low-Rank Fine-Tuning of Large Language Models.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Decentralized Low-Rank Fine-Tuning of Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:45.995786Z

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Observation 3fb4c442-8b3c-48e3-97fa-68224b76677b · outbound

This paper cites On the Convergence of Local Descent Methods in Federated Learning.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models On the Convergence of Local Descent Methods in Federated Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:46.062575Z

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Observation 4f7fcdf7-f724-4498-a503-ae1a20c241af · outbound

This paper cites A unified theory of decentralized sgd with changing topology and local updates.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models A unified theory of decentralized sgd with changing topology and local updates

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:58.848958Z

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Observation cd4f1f19-9f84-43b6-916a-7537a4fc842a · outbound

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

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Improving LoRA in Privacy-preserving Federated Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:46.239849Z

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Observation 38976448-b22b-4b01-bd5e-4d7044f75c0e · outbound

This paper cites FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:46.295564Z

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Observation 79a326fb-9188-4100-bd8e-6d1e1bbb64a7 · outbound

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

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Selective Aggregation for Low-Rank Adaptation in Federated Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:46.352200Z

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Observation 5e69d808-b505-44b2-a06c-e68c6b92cc76 · outbound

This paper cites DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:42:52.128674Z

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Observation a3b8ebe6-25cb-47b5-a891-f481d98e9ea9 · outbound

This paper cites Fast Updating Truncated SVD for Representation Learning with Sparse Matrices.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Fast Updating Truncated SVD for Representation Learning with Sparse Matrices

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:42:52.002123Z

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Observation 2e2940ff-04d3-474a-8a31-530476f1944c · outbound

This paper cites Llama 2: Open foundation and fine-tuned chat models, 2023.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Llama 2: Open foundation and fine-tuned chat models, 2023

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:58.478117Z

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Observation 9e08e8e9-fb8c-48c3-83cd-29e8c2cc37bd · outbound

This paper cites pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning

Reference 20

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Observation ee857587-801f-413d-b29a-2069005d5047 · outbound

This paper cites Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models.Advances in Neural Information Processing Systems, 36, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models.Advances in Neural Information Processing Systems, 36, 2024

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:58.145119Z

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Observation 2e18292c-02c4-4414-95ae-e1a6be27650b · outbound

This paper cites Personalized Collaborative Fine-Tuning for On-Device Large Language Models.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Personalized Collaborative Fine-Tuning for On-Device Large Language Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:42:51.869617Z

Source-reported events for the cited work

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Observation ef7c6e59-474d-41e7-98fb-1f31f660cba4 · outbound

This paper cites Learning multiple visual domains with residual adapters, 2017.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Learning multiple visual domains with residual adapters, 2017

Reference 23

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Observation fd5b8696-f918-4cc8-91d4-f10ac679bb0a · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning, 2022.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning, 2022

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:47.004590Z

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Observation b361c7c2-053c-4c46-9775-13f3a408df4e · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation, 2021.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Prefix-tuning: Optimizing continuous prompts for generation, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:57.784381Z

Source-reported events for the cited work

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Observation c03d5015-1c19-4dcc-8e5c-4806b832fcfb · outbound

This paper cites Aflora: Adaptive freezing of low rank adaptation in parameter efficient fine-tuning of large models, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Aflora: Adaptive freezing of low rank adaptation in parameter efficient fine-tuning of large models, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:57.433056Z

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Observation 49414622-fb2a-4468-919f-2ecbc9b10f6c · outbound

This paper cites LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:47.242333Z

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Observation e0b61838-6496-408e-8588-2e9aa86bb6fc · outbound

This paper cites LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:47.300407Z

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Observation 739294e1-2c5e-4388-b322-a82de4c69beb · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.Advances in Neural Information Processing Systems, 36, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Qlora: Efficient finetuning of quantized llms.Advances in Neural Information Processing Systems, 36, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:57.111982Z

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Observation b9742b43-af9b-4dc1-b7a5-ac1512aacfa2 · outbound

This paper cites QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:47.477726Z

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Observation dc3a332c-d708-4188-8e6b-53d89e68c140 · outbound

This paper cites Low-rank few-shot adaptation of vision-language models.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Low-rank few-shot adaptation of vision-language models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:47.583465Z

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Observation 7836c959-33cc-4cab-aa46-732e1ddbfefd · outbound

This paper cites Dimat: Decentralized iterative merging-and-training for deep learning models.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Dimat: Decentralized iterative merging-and-training for deep learning models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:56.817321Z

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Observation f50029b5-6eaa-43f8-a9f4-815ba8c9aa22 · outbound

This paper cites Dominating set model aggregation for communication-efficient decentralized deep learning.Neural Networks, 171:25–39, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Dominating set model aggregation for communication-efficient decentralized deep learning.Neural Networks, 171:25–39, 2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:56.481779Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c87f4611-4678-4deb-ac1e-a8b2fc3b9ab6 · outbound

This paper cites Collaborative deep learning in fixed topology networks.Advances in Neural Information Processing Systems, 30, 2017.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Collaborative deep learning in fixed topology networks.Advances in Neural Information Processing Systems, 30, 2017

Reference 34

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Observation 8af652ab-9ee5-4ece-b50c-b42299829a78 · outbound

This paper cites Cross-gradient aggregation for decentralized learning from non-iid data.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Cross-gradient aggregation for decentralized learning from non-iid data

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:56.195067Z

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Observation 2e916116-b987-4261-acd9-fa534855db1e · outbound

This paper cites Stochastic gradient push for distributed deep learning, 2019.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Stochastic gradient push for distributed deep learning, 2019

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:56.057244Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation de558e85-046c-4451-84c3-e6462533621e · outbound

This paper cites Personalized collaborative fine-tuning for on-device large language models, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Personalized collaborative fine-tuning for on-device large language models, 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:55.914928Z

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Observation 2de1701a-2fa7-4795-bb2f-b1149c2fc4ca · outbound

This paper cites MHRC: Closed-loop Decentralized Multi-Heterogeneous Robot Collaboration with Large Language Models.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models MHRC: Closed-loop Decentralized Multi-Heterogeneous Robot Collaboration with Large Language Models

Reference 38

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Observation 572739fd-1a44-49a1-b530-f0f3ec50c3ec · outbound

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DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Unresolved cited work

Reference 39

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Observation 76d8161c-add7-44b4-9bef-9a6102b33d01 · outbound

This paper cites A systematic literature review of blockchain-based federated learning: Architectures, applications and issues.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models A systematic literature review of blockchain-based federated learning: Architectures, applications and issues

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:55.696150Z

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Observation 799954f9-f8ee-4b1d-b5d4-78ae31c2146f · outbound

This paper cites Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:55.501000Z

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Observation d7420a58-a65e-4211-8d62-37db5f944ed7 · outbound

This paper cites Federated learning: Chal- lenges, methods, and future directions.IEEE signal processing magazine, 37(3):50–60, 2020.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Federated learning: Chal- lenges, methods, and future directions.IEEE signal processing magazine, 37(3):50–60, 2020

Reference 42

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Observation 47a8bf3b-365a-4475-bd6c-9aa8ea6f0876 · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:48.729186Z

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Observation 5463738d-73f1-4629-b66d-00c592af0801 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:48.816558Z

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Observation b6e86292-84ff-42e4-bce0-9b8060c5cdf5 · outbound

This paper cites Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:48.891650Z

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Observation 8e58af54-af00-4fe8-9b4d-ee4c19e28377 · outbound

This paper cites On nonconvex decentralized gradient descent.IEEE Transactions on signal processing, 66(11):2834–2848, 2018.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models On nonconvex decentralized gradient descent.IEEE Transactions on signal processing, 66(11):2834–2848, 2018

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:55.253801Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7645e22b-7d34-4b60-b07a-ecaa7880c5d6 · outbound

This paper cites Gradient tracking with multiple local sgd for decentralized non-convex learning.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Gradient tracking with multiple local sgd for decentralized non-convex learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:55.064661Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d3699a2f-9fce-433d-93b4-cb2d1f1dd081 · outbound

This paper cites Decentralized stochastic projection-free learning with compressed push-sum.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Decentralized stochastic projection-free learning with compressed push-sum

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:54.909071Z

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Observation d16f0108-77ed-41b4-80fe-336124c61a32 · outbound

This paper cites Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:49.217847Z

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Unavailable: canonical work link unavailable.

Observation 1d8e0ebc-653d-4607-a04a-bba9cbe494dc · outbound

This paper cites On the Convergence of FedAvg on Non-IID Data.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models On the Convergence of FedAvg on Non-IID Data

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:49.289558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6bfadfa-1f37-43f0-b6e0-1e3d206ff657 · outbound

This paper cites Communication-efficient algorithms for statistical optimization.Advances in neural information processing systems, 25, 2012.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Communication-efficient algorithms for statistical optimization.Advances in neural information processing systems, 25, 2012

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:49.360891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb76f176-6b95-47ec-8850-2e40aa598c17 · outbound

This paper cites Local SGD Converges Fast and Communicates Little.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Local SGD Converges Fast and Communicates Little

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:49.463427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2b5c3fb-1bd3-46ed-8bfd-cbef52f44130 · outbound

This paper cites Sparsified sgd with memory.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Sparsified sgd with memory

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:54.604093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d1a1ba11-7700-494a-ac97-31bf38ffe095 · outbound

This paper cites Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:54.351315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b75c1e3f-61b4-4cbe-bbde-7ba3fdaf9182 · outbound

This paper cites Openfedllm: Training large language models on decentralized private data via federated learning.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Openfedllm: Training large language models on decentralized private data via federated learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:54.155903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1668da68-c3b0-490b-a2a4-3a8de6d9ba41 · outbound

This paper cites Towards building the federatedgpt: Federated instruction tuning.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Towards building the federatedgpt: Federated instruction tuning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:49.769831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf7e19e9-4833-4dfd-ac12-6fe3dd502f16 · outbound

This paper cites Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:49.845365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44ff756c-018d-4466-afab-dc755df4809c · outbound

This paper cites Automated flower classification over a large number of classes.2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pages 722–729, 2008.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Automated flower classification over a large number of classes.2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pages 722–729, 2008

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:53.941376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 68022400-d295-4f7e-81c6-a83fcea2fa96 · outbound

This paper cites Ucf101: A dataset of 101 human actions classes from videos in the wild, 2012.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Ucf101: A dataset of 101 human actions classes from videos in the wild, 2012

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:49.990060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4daef8bf-027b-450b-af1c-476df82f6d10 · outbound

This paper cites Food-101 – mining discriminative components with random forests.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Food-101 – mining discriminative components with random forests

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:53.712971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f7c2af1d-9bdf-4299-9f01-7e41f531bdc6 · outbound

This paper cites Wic: the word-in-context dataset for evaluating context-sensitive meaning representations, 2019.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Wic: the word-in-context dataset for evaluating context-sensitive meaning representations, 2019

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:53.456187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8329617f-a4b4-44bc-ad6a-d4b5a7b03b8e · outbound

This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:50.251493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 564a35f3-52be-4078-990a-e9b7f89eae1e · outbound

This paper cites Flora: Low-rank adapters are secretly gradient compressors, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Flora: Low-rank adapters are secretly gradient compressors, 2024

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:53.252545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cedff49f-9487-47f6-b31a-b65905c35bb4 · outbound

This paper cites Federated lora with sparse communication, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Federated lora with sparse communication, 2024

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:50.430077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc32e377-483d-4672-bf56-c5555145ea95 · outbound

This paper cites FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:42:51.644386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c1647ca2-1d7a-48fd-ac08-44c894ea5494 · outbound

This paper cites Towards building the federated gpt: Federated instruction tuning, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Towards building the federated gpt: Federated instruction tuning, 2024

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:53.035386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a86ba44d-5354-4bc2-91e4-69225be44bef · outbound

This paper cites Openfedllm: Training large language models on decentralized private data via federated learning, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Openfedllm: Training large language models on decentralized private data via federated learning, 2024

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:52.879291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 645c3afa-c5e4-489b-bf1b-894710693f99 · outbound

This paper cites Differentially private low-rank adaptation of large language model using federated learning.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Differentially private low-rank adaptation of large language model using federated learning

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:50.728122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b2d047e-99f4-4d59-933e-668505ebeeeb · outbound

This paper cites On the kronecker product.Master’s thesis, University of Waterloo, 2004.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models On the kronecker product.Master’s thesis, University of Waterloo, 2004

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:52.715278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 455429ec-374f-43a9-9186-e1881b3fb81d · outbound

This paper cites Asymmetry in Low-Rank Adapters of Foundation Models.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Asymmetry in Low-Rank Adapters of Foundation Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:50.875629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8034bd56-823a-4a72-8930-34d4c3396252 · outbound

This paper cites On the linear speedup analysis of communication efficient momentum sgd for distributed non-convex optimization.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models On the linear speedup analysis of communication efficient momentum sgd for distributed non-convex optimization

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:50.961881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eda83eaa-b6db-43c3-a1bc-f6cbf69fb04c · outbound

This paper cites Local operator theory, random matrices and banach spaces.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Local operator theory, random matrices and banach spaces

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:52.552233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 693eaa6f-3560-4979-802d-dcdd6009e3c9 · outbound

This paper cites Flora: Low-Rank Adapters Are Secretly Gradient Compressors.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Flora: Low-Rank Adapters Are Secretly Gradient Compressors

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:51.100769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41f438c4-cca7-4e19-80a2-795e2e3c5cb4 · outbound

This paper cites Balancing communication and computation in distributed optimization.IEEE Transactions on Automatic Control, 64(8):3141–3155, 2018.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Balancing communication and computation in distributed optimization.IEEE Transactions on Automatic Control, 64(8):3141–3155, 2018

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:52.446496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 183fab69-c235-4148-bb6a-28c6cfc8c8ab · outbound

This paper cites Instruction tuning with gpt-4, 2023.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Instruction tuning with gpt-4, 2023

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:51.223220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e06e487b-90ef-453d-aea3-35d5529f1599 · outbound

This paper cites Xing, Hao Zhang, Joseph E.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Xing, Hao Zhang, Joseph E

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:51.318211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bfee431-f349-45e5-9a37-c7d4d8c09358 · outbound

This paper cites Gpt-4o system card, 2024.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Gpt-4o system card, 2024

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:51.396315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40e4e332-1785-4039-bc8a-7e979090c5d9 · outbound

This paper cites approximate.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models approximate

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:52.311877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Reference resolution

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