Paper Citation Record · LEDGER

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models

As of 9 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2506.09638.

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

pith.paper-citation-record.v1
2506.09638 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:46:48.850831Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T01:52:44.785582Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:46:56.783735Z

Reference resolution

42 of 42 outbound references displayed

verified exact1
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unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3687621b-3200-4b01-a764-79c25aeb6402 · outbound

This paper cites GPT-4 Technical Report.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models GPT-4 Technical Report

Reference 1

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Observation 4286ba1d-0d28-4e5d-b288-a4e2601deb45 · outbound

This paper cites FedMBridge: Bridgeable multimodal federated learning.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models FedMBridge: Bridgeable multimodal federated learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.415097Z

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Observation 7276e55d-b06b-4886-bccd-984f9872456f · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:46:48.693076Z

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Observation daaea0cb-42f9-4519-86e3-fcb340cf2af3 · outbound

This paper cites Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.395750Z

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Observation 558d49a0-913a-4979-ac04-943d7524c967 · outbound

This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:46:48.702039Z

Source-reported events for the cited work

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Observation 05525e87-928d-4b40-92be-854987948b73 · outbound

This paper cites Exploring the vulnerabilities of federated learning: A deep dive into gradient inversion attacks.arXiv preprint arXiv:2503.11514, 2025.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Exploring the vulnerabilities of federated learning: A deep dive into gradient inversion attacks.arXiv preprint arXiv:2503.11514, 2025

Reference 6

Resolution: verified exact; raw_fallback, observed 2026-08-07T04:46:49.015685Z

Source-reported events for the cited work

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Observation d94aa90f-a676-4eec-a61a-a57d319429b8 · outbound

This paper cites A new federated learning framework against gradient inversion attacks.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models A new federated learning framework against gradient inversion attacks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.383880Z

Source-reported events for the cited work

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Observation f88e9a82-ec7d-48d9-8f4b-8e7ce4bff44d · outbound

This paper cites Selective aggregation for low-rank adaptation in federated learning.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Selective aggregation for low-rank adaptation in federated learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.371984Z

Source-reported events for the cited work

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Observation d8e72b90-424c-4784-8947-f491ce13cc48 · outbound

This paper cites Measuring the effects of non-identical data distribu- tion for federated visual classification, 2019.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Measuring the effects of non-identical data distribu- tion for federated visual classification, 2019

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.359782Z

Source-reported events for the cited work

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Observation 14599aa7-b0d8-48e4-8a62-08b9b0d14aed · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:46:48.722162Z

Source-reported events for the cited work

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Observation 1cbae2c3-cedd-4128-afa3-4f4bfcbe2b30 · outbound

This paper cites Fedlps: Heterogeneous federated learning for multiple tasks with local parameter sharing, 2024.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Fedlps: Heterogeneous federated learning for multiple tasks with local parameter sharing, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.340328Z

Source-reported events for the cited work

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Observation ff54f841-c76d-44c9-92e9-604b57fc5664 · outbound

This paper cites an unresolved cited work.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Unresolved cited work

Reference 12

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Source-reported events for the cited work

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Observation fc7cd118-c8a0-42c2-941a-9ff54a03501a · outbound

This paper cites ReferItGame: Referring to objects in photographs of natural scenes.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models ReferItGame: Referring to objects in photographs of natural scenes

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.316564Z

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Observation 61969c35-083c-417d-a109-145ccb1ae660 · outbound

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

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.304569Z

Source-reported events for the cited work

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Observation 0d7733ea-24b1-4bcf-9f02-f63c684f800c · outbound

This paper cites A dataset of clinically generated visual questions and answers about radiology images.Scientific data, 5(1):1–10, 2018.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models A dataset of clinically generated visual questions and answers about radiology images.Scientific data, 5(1):1–10, 2018

Reference 15

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Source-reported events for the cited work

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Observation ed4a0e06-c688-453d-92da-1ed6e81bc56d · outbound

This paper cites Federated optimization in heterogeneous networks, 2020.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Federated optimization in heterogeneous networks, 2020

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.285106Z

Source-reported events for the cited work

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Observation c80410a0-366b-4f71-990b-2e05fc27441e · outbound

This paper cites UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T04:46:48.750294Z

Source-reported events for the cited work

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Observation 6fcec27f-9c28-4ef9-95eb-ace020d99d1d · outbound

This paper cites Lawrence Zitnick, and Piotr Dollár.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Lawrence Zitnick, and Piotr Dollár

Reference 18

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Source-reported events for the cited work

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Observation 17c332f1-4dad-48bd-8985-39e940a0f4fa · outbound

This paper cites Slake: A semantically-labeled knowledge-enhanced dataset for medical visual question answering, 2021.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Slake: A semantically-labeled knowledge-enhanced dataset for medical visual question answering, 2021

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.265909Z

Source-reported events for the cited work

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Observation 8573775a-0fbf-48d7-a371-401010216341 · outbound

This paper cites Visual instruction tuning.NIPS, 36:34892– 34916, 2023.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Visual instruction tuning.NIPS, 36:34892– 34916, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.253255Z

Source-reported events for the cited work

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Observation c7be9ac6-7605-470e-874b-3dbd90e7733d · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering, 2022.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Learn to explain: Multimodal reasoning via thought chains for science question answering, 2022

Reference 21

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Observation 9eed584a-cd20-4b2e-b846-be141e2870c4 · outbound

This paper cites Fine-grained visual classification of aircraft, 2013.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Fine-grained visual classification of aircraft, 2013

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.232649Z

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Observation 5d58df5e-ae06-44c5-a59a-cf82b6fd8b79 · outbound

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

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.220202Z

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Observation 421bfc29-05b1-4f7e-8f40-9dda2603b39b · outbound

This paper cites Introducing meta llama 3: The most capable openly available llm to date.Meta AI, 2024.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Introducing meta llama 3: The most capable openly available llm to date.Meta AI, 2024

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.207534Z

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Observation 5c311a02-a6b8-41c6-a168-db30a83f2495 · outbound

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

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Learning transferable visual models from natural language supervision

Reference 25

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Observation 5e48f7c5-2761-48e0-83c0-9592e2ac0fd5 · outbound

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

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Learning transferable visual models from natural language supervision, 2021

Reference 26

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Observation 0e0f3a57-d0fa-4ca9-aa7f-93a902cdf445 · outbound

This paper cites Adaptive Federated Optimization.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Adaptive Federated Optimization

Reference 27

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This paper cites Brendan McMahan.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Brendan McMahan

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.180748Z

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Observation cc5a492a-fd7d-48c7-80f0-a155200d9c34 · outbound

This paper cites Exploring models and data for image question answering, 2015.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Exploring models and data for image question answering, 2015

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.169097Z

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Observation 14ed9eac-a4d4-4169-a983-148c59da03fe · outbound

This paper cites Chameleon: Mixed-modal early-fusion foundation models, 2025.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Chameleon: Mixed-modal early-fusion foundation models, 2025

Reference 30

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Observation d3bc5996-47ec-47d4-a1f5-772b0e7cd7bb · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 31

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Observation 792201df-4f85-426e-9a7c-2f85b01f18a4 · outbound

This paper cites Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T04:46:48.811789Z

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Observation 41629199-3860-4d2d-9823-a4069c3ca8fe · outbound

This paper cites Pilot: Building the federated multimodal instruction tuning framework, 2025.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Pilot: Building the federated multimodal instruction tuning framework, 2025

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.149443Z

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Observation 0a106ab8-970c-4955-9bf5-39ff41f19d14 · outbound

This paper cites FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data

Reference 34

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Observation d98c9006-6fc1-4a8b-bf91-728615edf7a6 · outbound

This paper cites Fedmllm: Federated fine-tuning mllm on multimodal heterogeneity data, 2025.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Fedmllm: Federated fine-tuning mllm on multimodal heterogeneity data, 2025

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.137896Z

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Observation a9ac6d2c-1951-4bd6-b05f-5f200bbddce6 · outbound

This paper cites Fedllm- bench: Realistic benchmarks for federated learning of large language models.NIPS, 37:111106–111130, 2024.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Fedllm- bench: Realistic benchmarks for federated learning of large language models.NIPS, 37:111106–111130, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.126081Z

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Observation ebc7a116-8e1c-4e09-a4f3-768290500b7f · outbound

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

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Openfedllm: Training large language models on decentralized private data via federated learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.113860Z

Source-reported events for the cited work

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Observation 749aa389-b6ff-402c-9da6-e0a7d5239e37 · outbound

This paper cites Tackling data heterogeneity in federated learning via loss decomposition.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Tackling data heterogeneity in federated learning via loss decomposition

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.100515Z

Source-reported events for the cited work

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Observation b18696cc-86a5-4604-a243-908a3b9b6f80 · outbound

This paper cites Fedtgp: Trainable global prototypes with adaptive- margin-enhanced contrastive learning for data and model heterogeneity in federated learning, 2024.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Fedtgp: Trainable global prototypes with adaptive- margin-enhanced contrastive learning for data and model heterogeneity in federated learning, 2024

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.088407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 16c968b5-ec23-4c8c-b4b1-e3af78ab5aa6 · outbound

This paper cites Mllm- llava-fl: Multimodal large language model assisted federated learning.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Mllm- llava-fl: Multimodal large language model assisted federated learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.076280Z

Source-reported events for the cited work

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Observation b009af85-f999-4183-9a24-7a0db4f64bf3 · outbound

This paper cites Flhetbench: Benchmarking device and state heterogeneity in federated learning.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Flhetbench: Benchmarking device and state heterogeneity in federated learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.064475Z

Source-reported events for the cited work

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Observation c817fdf1-1cc3-47c7-80e7-c8b83804fafe · outbound

This paper cites Radgenome-chest ct: A grounded vision-language dataset for chest ct analysis, 2024.

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models Radgenome-chest ct: A grounded vision-language dataset for chest ct analysis, 2024

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:46:49.052242Z

Source-reported events for the cited work

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Pith citing papers

Observation 3c225ccd-48af-43ea-8c35-869ac0184beb · inbound

VTI-CoT: Visual-Textual Interleaved Chain of Thought for Video Reasoning cites this paper.

VTI-CoT: Visual-Textual Interleaved Chain of Thought for Video Reasoning FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:46:56.785169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.