Paper Citation Record · LEDGER

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation

As of 18 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2505.18494.

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

pith.paper-citation-record.v1
2505.18494 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:36:22.548468Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:16:49.110346Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T04:16:49.220173Z

Reference resolution

53 of 53 outbound references displayed

verified exact0
verified fuzzy30
unresolved23
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fc76bdf5-91ef-4412-93ed-c57e1b8aedf4 · outbound

This paper cites When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 1

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Observation bb174c85-0152-415b-858b-21b2c781f771 · outbound

This paper cites Advances and Open Challenges in Federated Foundation Models.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Advances and Open Challenges in Federated Foundation Models

Reference 2

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Observation 7b56ba8f-b98a-43b4-9b60-1e58421cbdb5 · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Communication-efficient learning of deep networks from decentralized data,

Reference 3

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Observation 37eea3cd-6a25-4018-b388-2bfafabc9380 · outbound

This paper cites Review on security of federated learning and its application in healthcare,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Review on security of federated learning and its application in healthcare,

Reference 4

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Observation ce04fa3e-c51e-46af-92d3-4c5f3e74e648 · outbound

This paper cites Ten challenging problems in federated founda- tion models,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Ten challenging problems in federated founda- tion models,

Reference 5

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Observation 5599707d-c61b-4311-90d4-85dac169674b · outbound

This paper cites Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning

Reference 6

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Observation 76be55b8-3897-439a-bc05-e8ede0a847c2 · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Promptfl: Let federated participants cooperatively learn prompts instead of models – federated learning in age of foundation model,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:27.062213Z

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Observation 3ece4557-b70a-4d33-ad72-d384dc764890 · outbound

This paper cites Fedbiot: Llm local fine-tuning in federated learning without full model,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Fedbiot: Llm local fine-tuning in federated learning without full model,

Reference 8

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Observation 0a3551c7-89ee-4f97-8bbf-06a27327e324 · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation LoRA: Low-Rank Adaptation of Large Language Models

Reference 9

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Observation 619cddef-91fe-4ca9-8445-751ea1a3702c · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:18.585797Z

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Observation 27002a5a-f5a9-4bbe-8fa8-d466e5d6e76e · outbound

This paper cites Low-parameter federated learning with large language models,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Low-parameter federated learning with large language models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.722174Z

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Observation 38db006e-7ec9-4361-b2fd-9b018b7ea99b · outbound

This paper cites Fedlore: Communication-efficient and personalized edge intelligence framework via federated low-rank estimation,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Fedlore: Communication-efficient and personalized edge intelligence framework via federated low-rank estimation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.548719Z

Source-reported events for the cited work

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Observation 19e84c90-3793-470d-9a63-9a9ffc746dfa · outbound

This paper cites Towards building the federatedgpt: Federated instruction tun- ing,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Towards building the federatedgpt: Federated instruction tun- ing,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.387696Z

Source-reported events for the cited work

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Observation 25843086-408e-49f3-8aa1-286b6612f44f · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Heterogeneous lora for federated fine-tuning of on-device foundation models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.229135Z

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 728320e8-15a4-43be-9784-601922045177 · outbound

This paper cites Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models

Reference 16

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Observation 8a7dabc0-ea4c-4169-a600-31e8a2a189fb · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Federated fine-tuning of large language models under heterogeneous tasks and client resources,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.951002Z

Source-reported events for the cited work

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Observation 2cd3cf4e-9806-422e-9feb-7d82dce2f025 · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs

Reference 18

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Observation 386e681c-2f3c-4932-8e92-dcd1dcd35128 · outbound

This paper cites Smooth diffusion: Crafting smooth latent spaces in diffusion models,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Smooth diffusion: Crafting smooth latent spaces in diffusion models,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.797624Z

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Observation 33d80cce-dfa0-4f91-9e6b-8ffb65be7a56 · outbound

This paper cites Autore: Document-level relation extraction with large language models,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Autore: Document-level relation extraction with large language models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.659994Z

Source-reported events for the cited work

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Observation b8d1a95e-ae79-4b55-b3a7-e1a27a8c4d98 · outbound

This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 21

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Observation c0268986-3f20-495f-b3a2-ff78b2e5492f · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Qlora: Efficient finetuning of quantized llms,

Reference 22

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Observation ae1be2ff-557f-4cd1-b825-2aa2f88e6184 · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine-tuning,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Adaptive budget allocation for parameter-efficient fine-tuning,

Reference 23

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Observation ac6a22bc-dea7-4757-9f4a-52df56b2005a · outbound

This paper cites LoRA+: Efficient low rank adaptation of large models,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation LoRA+: Efficient low rank adaptation of large models,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.360367Z

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Observation 9a931ea0-777c-4a64-897a-a286d0474af7 · outbound

This paper cites LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Reference 25

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Observation adbae732-0f17-4369-b302-8f422ad244a9 · outbound

This paper cites A kernel-based view of language model fine-tuning,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation A kernel-based view of language model fine-tuning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.210515Z

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Observation 0789745c-c253-4f3b-bb31-98647d5ae27b · outbound

This paper cites The expressive power of low-rank adaptation,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation The expressive power of low-rank adaptation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.060056Z

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Observation 6717d9a6-6619-4299-a110-2da68142b5c9 · outbound

This paper cites LoRA Training in the NTK Regime has No Spurious Local Minima.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation LoRA Training in the NTK Regime has No Spurious Local Minima

Reference 28

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Observation 2f24e509-0aef-4d9f-adac-562276c85ad7 · outbound

This paper cites Federated large language model: Solutions, challenges and future directions,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Federated large language model: Solutions, challenges and future directions,

Reference 29

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Observation 504551b8-b288-4e97-8743-6d27396277b7 · outbound

This paper cites A Survey on LoRA of Large Language Models.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation A Survey on LoRA of Large Language Models

Reference 30

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Observation d0231e11-4cc1-4c7c-9d09-9a4fd678875c · outbound

This paper cites Low-Parameter Federated Learning with Large Language Models.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Low-Parameter Federated Learning with Large Language Models

Reference 31

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Observation f305d27a-64df-4218-9b3b-d7e331dbb51e · outbound

This paper cites FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning

Reference 32

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Observation c8446d09-d368-40b0-a355-4b7678967da3 · outbound

This paper cites Hyperflora: Federated learning with instantaneous personalization,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Hyperflora: Federated learning with instantaneous personalization,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.578847Z

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Observation 51ae1085-60cc-4dd3-be3e-ae1f8dd907f6 · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Improving loRA in privacy-preserving federated learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.441620Z

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Observation db3f3a46-9f62-40f6-8264-1f10cc3fe74d · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Differentially private low-rank adaptation of large language model us- ing federated learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.315102Z

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Observation 4d059639-d2dd-4977-ba54-11109a142b5f · outbound

This paper cites Federated LoRA with Sparse Communication.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Federated LoRA with Sparse Communication

Reference 36

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Observation 32deb61e-0367-43ff-aadc-9f2cd44a13c9 · outbound

This paper cites Federated fine-tuning for pre-trained foundation models over wireless networks,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Federated fine-tuning for pre-trained foundation models over wireless networks,

Reference 37

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Observation ca4392a9-135a-43bb-8bbc-47183d9349b7 · outbound

This paper cites Fed- erated low-rank adaptation for large models fine-tuning over wireless networks,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Fed- erated low-rank adaptation for large models fine-tuning over wireless networks,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.189468Z

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Observation 28d96c87-c4c1-408b-8376-14c5879e7cfe · outbound

This paper cites Helora: Lora-heterogeneous federated fine-tuning for foundation models,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Helora: Lora-heterogeneous federated fine-tuning for foundation models,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.050149Z

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Observation 47d70d89-9a70-4cb3-afb0-3286cbb713c1 · outbound

This paper cites FLoRA: Federated fine-tuning large language models with heterogeneous low-rank adaptations,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation FLoRA: Federated fine-tuning large language models with heterogeneous low-rank adaptations,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.863094Z

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Observation a94d61b8-719c-4f3b-a0fd-da6e68d3bcac · outbound

This paper cites Fedfmsl: Federated learning of foundation models with sparsely activated lora,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Fedfmsl: Federated learning of foundation models with sparsely activated lora,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.093031Z

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Observation c4348a06-c253-475e-92d0-cf96b3b1f29b · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning

Reference 43

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Observation ce614171-3133-4971-ab0d-9f21b03014fe · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Training Verifiers to Solve Math Word Problems

Reference 44

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Observation 93084107-cd48-43b5-a795-9d3fd4f0d3c3 · outbound

This paper cites Code alpaca: an instruction-following llama model for code generation, 2023,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Code alpaca: an instruction-following llama model for code generation, 2023,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.746086Z

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Observation 6cac4c79-9839-4cd2-afb5-2a0d1827722c · outbound

This paper cites Evaluating Large Language Models Trained on Code.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Evaluating Large Language Models Trained on Code

Reference 46

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Observation eb4a784d-dcd3-4dde-92aa-2bf02aaf5e88 · outbound

This paper cites Dolly: Democratizing the magic of chatgpt with open models,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Dolly: Democratizing the magic of chatgpt with open models,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.633293Z

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Observation b9b4cc7b-b0b7-4988-8a5a-0c6a37758fb9 · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.506654Z

Source-reported events for the cited work

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Observation 64496435-921a-467a-a194-b8189aba4be2 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 49

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Observation 4771bf6b-6bc4-435e-92e9-9efdb7ca0ea9 · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning,

Reference 50

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Observation 72c7d323-802d-4636-b5be-1dea9cc835a6 · outbound

This paper cites Ferret: Federated full-parameter tuning at scale for large language models,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Ferret: Federated full-parameter tuning at scale for large language models,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.385287Z

Source-reported events for the cited work

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Observation 3cd16765-da6d-4747-adb1-765b9b0134ac · outbound

This paper cites On the convergence of zeroth-order federated tuning for large language models,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation On the convergence of zeroth-order federated tuning for large language models,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.221119Z

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 31b44467-07d3-4ea4-ad20-e0ea1b63efa3 · outbound

This paper cites Local sgd converges fast and communicates little,.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Local sgd converges fast and communicates little,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.083399Z

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 f0998cd5-8c81-4fc6-9f4f-b89666b9aae1 · outbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Parallel restarted sgd with faster con- vergence and less communication: Demystifying why model averaging works for deep learning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:22.889118Z

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 224db9f2-4415-4db9-81fb-412449372094 · outbound

This paper cites Available: https://openreview.net/forum?id=likXVjmh3E.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Available: https://openreview.net/forum?id=likXVjmh3E

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.899812Z

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.

Pith citing papers

Observation 7845cf69-d643-4bb9-86bc-9faa59b845b1 · inbound

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks cites this paper.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-08-14T04:16:49.227782Z

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.