FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE | Paper Citation Record

Observation 0a014456-dca8-4eeb-bca0-e9c72a570349 · outbound

This paper cites GPT-4 Technical Report.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE GPT-4 Technical Report

Reference 1

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This paper cites Fine-tuning large language models for specialized use cases.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Fine-tuning large language models for specialized use cases

Reference 2

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Observation 0436d0c6-75af-4b8c-ba50-f0f6885ff3a3 · outbound

This paper cites Federated fine-tuning of large language models under heterogeneous tasks and client resources.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Federated fine-tuning of large language models under heterogeneous tasks and client resources

Reference 3

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Observation 3717be6b-0776-42b3-b5ab-db9df47284fd · outbound

This paper cites FedAdapter: Efficient Federated Learning for Modern NLP.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE FedAdapter: Efficient Federated Learning for Modern NLP

Reference 4

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This paper cites A survey on mixture of experts in large language models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE A survey on mixture of experts in large language models

Reference 5

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This paper cites Improved training of mixture-of-experts language gans.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Improved training of mixture-of-experts language gans

Reference 6

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Observation 22c4735f-05a5-492b-bb01-4ae23bb39f5d · outbound

This paper cites Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization

Reference 7

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Observation 43fbb149-fcc2-4be3-84fd-0106681701d2 · outbound

This paper cites Alpagasus: Training a better alpaca with fewer data.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Alpagasus: Training a better alpaca with fewer data

Reference 8

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Observation ecc81d5d-252c-4c36-856a-e5fbf090c530 · outbound

This paper cites LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs

Reference 9

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Observation 0cfd8835-fe78-445e-a9e5-be536f650d9a · outbound

This paper cites Robust federated finetuning of llms via alternating optimization of lora.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Robust federated finetuning of llms via alternating optimization of lora

Reference 10

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Observation aeefbf24-e315-4ee6-b8a4-9f3c50a0bb63 · outbound

This paper cites Heterogeneous LoRA for federated fine-tuning of on-device foundation models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Heterogeneous LoRA for federated fine-tuning of on-device foundation models

Reference 11

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Observation 5f869d02-ab42-4021-b1ff-ffe482f0cdb7 · outbound

This paper cites Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023

Reference 12

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Observation 37687138-a24b-4f14-96c9-e67fa9c3b59a · outbound

This paper cites Towards next-generation intelligent assistants leveraging llm techniques.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Towards next-generation intelligent assistants leveraging llm techniques

Reference 13

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Observation 12f15a85-fa72-43ac-b219-05cfc7fea3fc · outbound

This paper cites Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs

Reference 14

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Observation 463c25d6-b11d-4cee-84fe-f9b38f9a3c7d · outbound

This paper cites Pm-moe: Mixture of experts on private model parameters for personalized federated learning.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Pm-moe: Mixture of experts on private model parameters for personalized federated learning

Reference 15

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Observation 58426aa9-67d3-48b5-9f44-d4264f6dac2f · outbound

This paper cites QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Reference 16

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Observation ede50525-0104-4a1c-a41b-ff2da94d2483 · outbound

This paper cites Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models

Reference 17

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Observation 375f7572-335b-4e05-bb86-619989692f22 · outbound

This paper cites OLMo: Accelerating the Science of Language Models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE OLMo: Accelerating the Science of Language Models

Reference 18

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Observation e3e4fb53-2ed7-483b-95dc-0a1d091eece6 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 19

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Observation fdb83ebc-6cea-4620-bacf-22bb67dbfa44 · outbound

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

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Selective Aggregation for Low-Rank Adaptation in Federated Learning

Reference 20

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Observation a8517bce-1ed0-4ccb-96dc-65b5cf895343 · outbound

This paper cites Promptfl: Let feder- ated participants cooperatively learn prompts instead of models–federated learning in age of foundation model.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Promptfl: Let feder- ated participants cooperatively learn prompts instead of models–federated learning in age of foundation model

Reference 21

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Observation a10aa7d0-8e3d-43b1-888b-bc31f7cc68b6 · outbound

This paper cites Language model compression with weighted low-rank factorization.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Language model compression with weighted low-rank factorization

Reference 22

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Observation cca72b09-a5c1-43f9-b039-b1d95c7c1593 · outbound

This paper cites Numerical optimizations for weighted low-rank estimation on language models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Numerical optimizations for weighted low-rank estimation on language models

Reference 23

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Observation ae31e37e-ed3f-4df5-9ef2-8f131bbdde66 · outbound

This paper cites Bing chat: The future of search engines? Proceedings of the Association for Information Science and Technology, 60(1):1007–1009, 2023.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Bing chat: The future of search engines? Proceedings of the Association for Information Science and Technology, 60(1):1007–1009, 2023

Reference 24

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Observation 555e7981-6754-49ec-9f74-302eba4e5747 · outbound

This paper cites Client- customized adaptation for parameter-efficient federated learning.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Client- customized adaptation for parameter-efficient federated learning

Reference 25

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Observation 4a0438b0-0d0c-4699-8f4a-71b8a629eb63 · outbound

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

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning

Reference 26

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Observation 826121d4-668e-40fb-9aa0-8d9ae6e89a20 · outbound

This paper cites Survey of Dropout Methods for Deep Neural Networks.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Survey of Dropout Methods for Deep Neural Networks

Reference 27

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Observation da79272c-b05f-420f-b47f-6d08fddd957d · outbound

This paper cites On the convergence of fedavg on non-iid data.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE On the convergence of fedavg on non-iid data

Reference 28

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Observation 380bf2fe-c413-4694-9c0e-211d8df38000 · outbound

This paper cites DeepSeek-V3 Technical Report.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE DeepSeek-V3 Technical Report

Reference 29

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This paper cites Unlocking personalized knowledge in federated large language model: The power of mixture of experts.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Unlocking personalized knowledge in federated large language model: The power of mixture of experts

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Observation f7583ee5-2617-4193-a549-9fb1bbd1491d · outbound

This paper cites Improved baselines with visual instruction tuning.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Improved baselines with visual instruction tuning

Reference 31

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Observation 99b2aee5-fa8a-4eaf-93ca-c5e972db9943 · outbound

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

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Communication-efficient learning of deep networks from decentralized data

Reference 32

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This paper cites The llama 4 herd: The beginning of a new era of natively multimodal ai innovation.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE The llama 4 herd: The beginning of a new era of natively multimodal ai innovation

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This paper cites OLMoE: Open Mixture-of-Experts Language Models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE OLMoE: Open Mixture-of-Experts Language Models

Reference 34

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Observation 89c6eaec-dc4c-4881-911e-d1c9352ae3fc · outbound

This paper cites The california consumer privacy act: Towards a european-style privacy regime in the united states.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE The california consumer privacy act: Towards a european-style privacy regime in the united states

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Observation 5e7d4271-c0b8-475c-a14a-fad48a945504 · outbound

This paper cites The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 36

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This paper cites FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models

Reference 37

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This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

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This paper cites Dropout: a simple way to prevent neural networks from overfitting.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Dropout: a simple way to prevent neural networks from overfitting

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This paper cites FedBPT: Efficient federated black-box prompt tuning for large language models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE FedBPT: Efficient federated black-box prompt tuning for large language models

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Observation ee0c0137-0a5d-4fd1-8700-203ff8d6b117 · outbound

This paper cites Improving loRA in privacy-preserving federated learning.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Improving loRA in privacy-preserving federated learning

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This paper cites Large language models in medicine.Nature medicine, 29(8):1930–1940, 2023.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Large language models in medicine.Nature medicine, 29(8):1930–1940, 2023

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This paper cites Revisiting sparse mixture of experts for resource- adaptive federated fine-tuning foundation models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Revisiting sparse mixture of experts for resource- adaptive federated fine-tuning foundation models

Reference 43

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Observation eb85f41e-b005-44ce-981b-606c3436d238 · outbound

This paper cites The eu general data protection regulation (gdpr).A practical guide, 1st ed., Cham: Springer International Publishing, 10(3152676):10–5555, 2017.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE The eu general data protection regulation (gdpr).A practical guide, 1st ed., Cham: Springer International Publishing, 10(3152676):10–5555, 2017

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

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

Reference 45

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

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Federated fine-tuning of llms on the very edge: The good, the bad, the ugly

Reference 46

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Observation 44212c8b-323c-422d-977e-35b79393e609 · outbound

This paper cites A survey on federated fine-tuning of large language models.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE A survey on federated fine-tuning of large language models

Reference 47

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This paper cites FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition

Reference 48

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This paper cites Fedhm: Efficient federated learning for heterogeneous models via low-rank factorization.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Fedhm: Efficient federated learning for heterogeneous models via low-rank factorization

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:27:28.461501Z

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Observation 4806fefc-b81a-415f-ab90-2bdf1d38cb0d · outbound

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

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Towards building the federatedgpt: Federated instruction tuning

Reference 50

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

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE FedPETuning: When federated learning meets the parameter-efficient tuning methods of pre- trained language models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:27:28.438642Z

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Observation 54ccf66a-d7d1-408d-a563-7bc85ce97dc8 · outbound

This paper cites AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T19:27:27.793938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d60a8e88-9085-4605-b45b-06cbc569bcc2 · outbound

This paper cites No rescaler.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE No rescaler

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:27:28.424096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation edde92cd-e504-44ab-9748-fa4a6d952747 · outbound

This paper cites an unresolved cited work.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:27:28.410834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 337e33b3-088a-4d38-ace6-96a45fa1ddaa · outbound

This paper cites Harnessing SMoE architecture.

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE Harnessing SMoE architecture

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:27:28.396994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers