Paper Citation Record · LEDGER

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data

As of 18 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 3 inbound Pith citation observations for arXiv:2411.14717.

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

pith.paper-citation-record.v1
2411.14717 v2

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

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measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:46:48.820248Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T14:58:44.663781Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3668a6ad-a362-4ee1-baeb-a8071932e864 · outbound

This paper cites GPT-4 Technical Report.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data GPT-4 Technical Report

Reference 1

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Observation ec58714e-4580-4193-b1ac-6cb62c60c112 · outbound

This paper cites Crisis- mmd: Multimodal twitter datasets from natural disasters.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Crisis- mmd: Multimodal twitter datasets from natural disasters

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.366279Z

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Observation 8943f1ea-45bc-4d50-a8b0-21e1355fbaf6 · outbound

This paper cites Gemma: Introducing new state-of-the-art open models.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Gemma: Introducing new state-of-the-art open models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.354510Z

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Observation 22c94d9c-7415-4a49-84da-207fb2e0ef9b · outbound

This paper cites Leveraging foundation models for multi-modal federated learning with incomplete modality.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Leveraging foundation models for multi-modal federated learning with incomplete modality

Reference 4

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Observation f35ef5d0-91c5-4af5-b8c7-1d16e9d9fe05 · outbound

This paper cites Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning

Reference 5

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Observation 54cb3c6b-78e1-4b19-8cf2-a11fb385ff08 · outbound

This paper cites Fedmsplit: Correlation- adaptive federated multi-task learning across multimodal split networks.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Fedmsplit: Correlation- adaptive federated multi-task learning across multimodal split networks

Reference 6

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Observation 8306bbbe-fdb2-44b9-9e74-a2363f9ac03e · outbound

This paper cites On disentanglement of asym- metrical knowledge transfer for modality-task agnostic fed- erated learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data On disentanglement of asym- metrical knowledge transfer for modality-task agnostic fed- erated learning

Reference 7

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Observation e7cc9180-66ba-49c3-8c81-5f9806aff5d9 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

Reference 8

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Observation 1ff84663-6d6c-46cc-ba93-cff12e9c44a1 · outbound

This paper cites Adaptive sub- gradient methods for online learning and stochastic opti- mization.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Adaptive sub- gradient methods for online learning and stochastic opti- mization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.290692Z

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Observation bf900659-a3e7-41bb-96a6-1ea483cd5f6b · outbound

This paper cites Fedmultimodal: A bench- mark for multimodal federated learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Fedmultimodal: A bench- mark for multimodal federated learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.280807Z

Source-reported events for the cited work

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Observation 1cef9196-940d-4fd8-abc7-2934620fdc11 · outbound

This paper cites MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

Reference 11

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Observation 23852076-ae8a-4e08-b41b-c777bf4148d8 · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 12

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Observation 60dbe802-0d84-4ff7-9de5-a7d40f1f0341 · outbound

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

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

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Observation 6c50f098-16cd-460d-a244-6fc7eeb351ee · outbound

This paper cites Cross-Silo Federated Learning: Challenges and Opportunities.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Cross-Silo Federated Learning: Challenges and Opportunities

Reference 14

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Observation b3543880-54dc-48bd-bcc6-0c84ac05e20e · outbound

This paper cites Federated learning for general- ization, robustness, fairness: A survey and benchmark.IEEE TPAMI, 2024.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Federated learning for general- ization, robustness, fairness: A survey and benchmark.IEEE TPAMI, 2024

Reference 15

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Source-reported events for the cited work

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Observation 055cb591-9440-4835-a891-68f64ac4b554 · outbound

This paper cites Phi-2: The surprising power of small language models.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Phi-2: The surprising power of small language models

Reference 16

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Source-reported events for the cited work

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Observation d7c3f0f5-a2e1-442c-a660-e064fd8ee2ca · outbound

This paper cites Efficient multimodal large language models: A survey.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Efficient multimodal large language models: A survey

Reference 17

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Observation 086bc578-31ef-4f7e-9998-3d9b241066fb · outbound

This paper cites Advances and open problems in federated learn- ing.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Advances and open problems in federated learn- ing

Reference 18

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Observation 6fbb9b9f-764a-41b3-b276-872d964234c1 · outbound

This paper cites Scaffold: Stochastic controlled averaging for fed- erated learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Scaffold: Stochastic controlled averaging for fed- erated learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.234867Z

Source-reported events for the cited work

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Observation 3caf06be-968b-4640-aedb-f80b32cd6ac1 · outbound

This paper cites The hateful memes challenge: Detecting hate speech in multimodal memes.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data The hateful memes challenge: Detecting hate speech in multimodal memes

Reference 20

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Observation 570def79-02a1-429d-b6b7-fc5df001f43e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Adam: A Method for Stochastic Optimization

Reference 21

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Observation ab23f535-c28e-4f46-85a0-c9dc7698eb70 · outbound

This paper cites Federatedscope-llm: A comprehen- sive package for fine-tuning large language models in feder- ated learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Federatedscope-llm: A comprehen- sive package for fine-tuning large language models in feder- ated learning

Reference 22

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Observation f85ea5bb-5bdd-46b2-8309-28f50164172d · outbound

This paper cites A dataset of clinically generated visual questions and answers about radiology images.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data A dataset of clinically generated visual questions and answers about radiology images

Reference 23

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Observation 0b21540c-d701-42e5-bd4e-a1455bfaacc5 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 24

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Observation cf972aa0-97cb-4c1e-b702-be063e2fdd51 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 25

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Observation c4bbc7a7-8468-49b1-b86d-e571f3ba52d5 · outbound

This paper cites A review of applications in federated learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data A review of applications in federated learning

Reference 26

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Observation 69884c79-ec2b-405a-a604-913fed0baf4c · outbound

This paper cites Federated optimiza- tion in heterogeneous networks.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Federated optimiza- tion in heterogeneous networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.172161Z

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Observation 1cd11abf-6340-4ac1-9842-233d0782de92 · outbound

This paper cites Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Reference 28

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Observation baaac2c4-e501-4d43-8582-ee81a88acf07 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.159649Z

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Observation 021a8567-02c0-4da3-9449-4c72958a7bbd · outbound

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

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Slake: A semantically-labeled knowledge- enhanced dataset for medical visual question answering

Reference 30

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Observation f3678680-a643-4747-adf0-cf3eb10e24ed · outbound

This paper cites PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging

Reference 31

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Observation 497f07d4-f39b-4755-8de1-48c63ef3c5cd · outbound

This paper cites Visual instruction tuning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Visual instruction tuning

Reference 32

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Observation e7cce1c8-aab2-44bc-a771-4b545e7e858f · outbound

This paper cites MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Reference 33

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Observation 6e470ce9-fa3d-4848-ac44-12f032b6c15f · outbound

This paper cites Smil: Multimodal learning with severely missing modality.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Smil: Multimodal learning with severely missing modality

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.132242Z

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Observation 079f4d58-8911-49e7-add9-a7e32ceca2e2 · outbound

This paper cites Federated Learning: Opportunities and Challenges.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Federated Learning: Opportunities and Challenges

Reference 35

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Observation 576638b4-085f-47cb-bc7c-518bd9ecba01 · outbound

This paper cites Communication- efficient learning of deep networks from decentralized data.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Communication- efficient learning of deep networks from decentralized data

Reference 36

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Observation 58cb109d-24da-4a54-a83f-993b8a7cdd71 · outbound

This paper cites Introducing chatgpt.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Introducing chatgpt

Reference 37

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Observation 75ca3967-ebef-4c14-b922-03719004c445 · outbound

This paper cites Adaptive hyper-graph aggregation for modality-agnostic federated learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Adaptive hyper-graph aggregation for modality-agnostic federated learning

Reference 38

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Observation 03d92b8e-824b-4fe0-aa8e-4bb3f6233a5f · outbound

This paper cites Adaptive federated optimization.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Adaptive federated optimization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.091678Z

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Observation 507d6e16-c063-486e-aba3-72171951aebc · outbound

This paper cites L-dawa: Layer-wise divergence aware weight ag- gregation in federated self-supervised visual representation learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data L-dawa: Layer-wise divergence aware weight ag- gregation in federated self-supervised visual representation learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.081075Z

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Observation 51a5c18a-a999-479e-86d6-e290e35c095d · outbound

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

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Gemini: A Family of Highly Capable Multimodal Models

Reference 41

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Observation cd565641-992a-4a69-bc4e-3c9e40d968a1 · outbound

This paper cites Position: Will we run out of data? limits of llm scaling based on human- generated data.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Position: Will we run out of data? limits of llm scaling based on human- generated data

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.070177Z

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Observation ae16d555-8c51-4f41-80b2-aba6c4a8ed39 · outbound

This paper cites Multimodal large language models: A sur- vey.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Multimodal large language models: A sur- vey

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.058756Z

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Observation d7b97434-9468-42be-8fd3-e9f9ca84b2f3 · outbound

This paper cites Pilot: Building the Federated Multimodal Instruction Tuning Framework.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Pilot: Building the Federated Multimodal Instruction Tuning Framework

Reference 44

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Observation b6485fe5-cb52-4a5a-8689-a77022528262 · outbound

This paper cites Cross-modal federated human activity recogni- tion via modality-agnostic and modality-specific representa- tion learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Cross-modal federated human activity recogni- tion via modality-agnostic and modality-specific representa- tion learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.047651Z

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Observation a4c6d83c-4c6c-4f7a-a154-6cc8b5c6c29e · outbound

This paper cites Cross-modal federated human activity recogni- tion.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Cross-modal federated human activity recogni- tion

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.036635Z

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Observation 982d7c0c-f9f7-4a93-bbbf-ee1ee4f86a1c · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 47

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Observation 8c17f4e3-8103-45d9-b406-fa9644b1d020 · outbound

This paper cites Open- fedllm: Training large language models on decentralized pri- vate data via federated learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Open- fedllm: Training large language models on decentralized pri- vate data via federated learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.024936Z

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Observation f60f71ea-cb9d-47b2-9654-bab01cfbf44d · outbound

This paper cites Fedllm-bench: Re- alistic benchmarks for federated learning of large language models.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Fedllm-bench: Re- alistic benchmarks for federated learning of large language models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.013720Z

Source-reported events for the cited work

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Observation 3b42b674-2f83-4feb-84e3-dad441382450 · outbound

This paper cites A Survey on Multimodal Large Language Models.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data A Survey on Multimodal Large Language Models

Reference 50

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Observation 615032d4-6a4c-40e2-b722-28ef5f11ef53 · outbound

This paper cites Multimodal federated learning via contrastive representation ensemble.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Multimodal federated learning via contrastive representation ensemble

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:15.001855Z

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Observation df03c35f-7e94-46aa-913c-fb58cb97eae4 · outbound

This paper cites Adaptive methods for nonconvex optimization.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Adaptive methods for nonconvex optimization

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:14.990842Z

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Observation 3360f50e-ed4a-4912-8fe7-f6a7feb76119 · outbound

This paper cites To- wards building the federatedgpt: Federated instruction tun- ing.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data To- wards building the federatedgpt: Federated instruction tun- ing

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:14.980829Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation af7b2120-29da-43a4-8320-117d71454522 · outbound

This paper cites MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning

Reference 54

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Observation 241488aa-8bda-446f-a187-71ae1622726a · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T15:04:14.601150Z

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FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data b) Multimodal Federated Learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:14.969810Z

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Observation a94fa960-66ff-4b44-ac07-57f27621366f · outbound

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FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:04:14.957332Z

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Observation 842bf8d8-8407-4e42-bef5-7b224d5285de · outbound

This paper cites a) Configurations.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data a) Configurations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:14.944834Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d7e99775-62d6-4d03-b201-d0f9f9e978ff · outbound

This paper cites The rapid develop- ment of Large Language Models (LLMs) has significantly driven the research surge in Multimodal Large Language Models (MLLMs) [50].

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data The rapid develop- ment of Large Language Models (LLMs) has significantly driven the research surge in Multimodal Large Language Models (MLLMs) [50]

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:14.933535Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9de52cde-2778-4156-91d1-30dae6fa9de3 · outbound

This paper cites In this study, we only use the training set and the test set.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data In this study, we only use the training set and the test set

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:14.921929Z

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Observation 806a0935-94c1-4fb4-b9df-487badf1b374 · outbound

This paper cites an unresolved cited work.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data Unresolved cited work

Reference 61

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e014fb79-a7e7-4b5c-a318-a3f2ddf81531 · outbound

This paper cites id”, “image.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data id”, “image

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:04:14.897303Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

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

FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models cites this paper.

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 3f6848da-d263-4221-a4dc-2cea19d1d396 · inbound

FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models cites this paper.

FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data

Reference 28

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Observation 50e0c0df-1fa7-4c8f-b19f-bb0dcda53901 · inbound

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation cites this paper.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.