Paper Citation Record · LEDGER

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation

As of 8 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 2 inbound Pith citation observations for arXiv:2506.12213.

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

pith.paper-citation-record.v1
2506.12213 v1

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:03:43.510839Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:27:20.977277Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T12:01:21.159338Z

Reference resolution

72 of 72 outbound references displayed

verified exact2
verified fuzzy44
unresolved26
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8b4e0eec-a46c-4038-b810-894ec97e1a4a · outbound

This paper cites Slimfit: Memory-efficient fine-tuning of transformer-based models using training dynamics.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Slimfit: Memory-efficient fine-tuning of transformer-based models using training dynamics

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:52.130098Z

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Observation 4d5cd222-702f-417e-b790-33414a3c2816 · outbound

This paper cites SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models

Reference 2

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Observation 7bc90568-4e2d-467b-bb52-e3ae36337455 · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Federated fine-tuning of large language models under heterogeneous tasks and client resources

Reference 3

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Observation bcf4e40b-b1e9-4ba9-a3b0-f29787831884 · outbound

This paper cites Strong baselines for parameter-efficient few-shot fine-tuning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Strong baselines for parameter-efficient few-shot fine-tuning

Reference 4

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Observation 8cbcfdc2-4054-4094-96d5-10a3190693a5 · outbound

This paper cites Practical Secure Aggregation for Federated Learning on User-Held Data.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Practical Secure Aggregation for Federated Learning on User-Held Data

Reference 5

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Observation df6ce00a-8ad8-464b-b7ad-72885dfea219 · outbound

This paper cites Fltrust: Byzantine- robust federated learning via trust bootstrapping.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fltrust: Byzantine- robust federated learning via trust bootstrapping

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:51.839644Z

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Observation a1176ad8-17de-4287-a355-ab5fa6a49f80 · outbound

This paper cites Lexglue: A benchmark dataset for legal language understanding in english.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Lexglue: A benchmark dataset for legal language understanding in english

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:51.632949Z

Source-reported events for the cited work

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Observation 74f873f9-6007-4259-b796-82e0a260cf28 · outbound

This paper cites Data-juicer: A one-stop data processing system for large language mod- els.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Data-juicer: A one-stop data processing system for large language mod- els

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:51.447263Z

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Observation 3fc00bf3-ff39-44d4-a29e-b69ac911e159 · outbound

This paper cites Which layer is learning faster? a systematic exploration of layer-wise convergence rate for deep neural networks.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Which layer is learning faster? a systematic exploration of layer-wise convergence rate for deep neural networks

Reference 9

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Observation cb0de46d-5b1d-4ada-9b06-4e1ef562f272 · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Heterogeneous lora for federated fine-tuning of on-device foundation models

Reference 10

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Observation b76333bd-1ea7-4c6a-a767-bad8e68d910c · outbound

This paper cites Describing textures in the wild.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Describing textures in the wild

Reference 11

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Observation ee10b146-a550-4a49-8ee5-199b56a58c56 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Imagenet: A large-scale hierarchical image database

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:50.731778Z

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Observation e3775fb4-0f18-411c-9bfd-4f8080b45778 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language un- derstanding.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Bert: Pre-training of deep bidirectional transformers for language un- derstanding

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:50.584391Z

Source-reported events for the cited work

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Observation 0c0c3232-1349-4c05-8055-f50e5b46b3d2 · outbound

This paper cites Heterofl: Computation and communication efficient federated learning for heterogeneous clients.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Heterofl: Computation and communication efficient federated learning for heterogeneous clients

Reference 14

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Observation 1f2f099d-e45e-498f-978d-0f5dd9861fbe · outbound

This paper cites Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 15

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Observation 32cd7649-b445-473a-8dc7-be71a59b801b · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:50.211739Z

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Observation c4c4a4b1-aeca-465f-97a5-3f73728454b8 · outbound

This paper cites Differential privacy.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Differential privacy

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:49.999008Z

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Observation c4b804e3-00e2-4788-be91-e4c0fac1e69f · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Model inversion attacks that exploit confidence information and basic countermeasures

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:49.801454Z

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Observation 77c9ff5f-7e2f-42a9-8c68-e0658d24ca2f · outbound

This paper cites Higher Layers Need More LoRA Experts.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Higher Layers Need More LoRA Experts

Reference 19

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Observation 529a47af-9c32-492e-8b11-f7e86800cb1f · outbound

This paper cites Flowertune: A cross-domain benchmark for federated fine-tuning of large language models.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Flowertune: A cross-domain benchmark for federated fine-tuning of large language models

Reference 20

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Observation 58fadb28-c4be-4419-862a-9c102892e4a8 · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Promptfl: Let federated participants cooperatively learn prompts instead of models-federated learning in age of foundation model

Reference 21

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Observation 553ae504-fd21-4e8b-9d68-c5644ca9ebaa · outbound

This paper cites Algorithm as 136: A k-means clustering algorithm.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Algorithm as 136: A k-means clustering algorithm

Reference 22

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Observation 37c01335-48f4-46a8-9322-e2e6ffdba2c4 · outbound

This paper cites Towards a unified view of parameter-efficient transfer learning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Towards a unified view of parameter-efficient transfer learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:49.403433Z

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Observation ef17cf0b-9e0b-459e-bd56-3b449d958e54 · outbound

This paper cites Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout

Reference 24

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Observation b7be1bb9-0d74-43cf-9dc3-c2a3d5623e47 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Lora: Low-rank adaptation of large language models

Reference 25

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Observation d85d0228-c339-47c7-b9cf-ff35b18f06b7 · outbound

This paper cites Depthfl: Depthwise federated learning for heterogeneous clients.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Depthfl: Depthwise federated learning for heterogeneous clients

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:49.215613Z

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Observation cac0e256-df44-42b9-851c-c6f484d2cf39 · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Federated Learning: Strategies for Improving Communication Efficiency

Reference 27

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Observation d2d07269-708d-42ed-9d79-94fe627f70b4 · outbound

This paper cites Learning multiple layers of features from tiny images.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Learning multiple layers of features from tiny images

Reference 28

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Observation 176e71e5-928d-4bdc-9f33-3a50f4a80047 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation The power of scale for parameter-efficient prompt tuning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:49.058983Z

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Observation f36b29f5-c643-46ef-abc7-ca222c954270 · outbound

This paper cites FedMD: Heterogenous Federated Learning via Model Distillation.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation FedMD: Heterogenous Federated Learning via Model Distillation

Reference 30

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Observation 36b193ef-2a59-4b6b-994b-33cd7f20a58e · outbound

This paper cites Fedtp: Federated learning by transformer personalization.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fedtp: Federated learning by transformer personalization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:48.884642Z

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Observation 8ee30348-d802-42a6-8547-e1d264d62364 · outbound

This paper cites Rouge: A package for automatic evaluation of sum- maries.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Rouge: A package for automatic evaluation of sum- maries

Reference 32

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Observation e0febcc7-3467-4a0e-a28e-87c57c31d32a · outbound

This paper cites No one left behind: Inclusive federated learning over heterogeneous devices.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation No one left behind: Inclusive federated learning over heterogeneous devices

Reference 33

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Differentially private low-rank adaptation of large language model using federated learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:48.702199Z

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Observation 7ac34597-0b13-45ef-8bc4-9bc0efe39a7e · outbound

This paper cites On surgical fine-tuning for language encoders.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation On surgical fine-tuning for language encoders

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:48.518299Z

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Observation ba6455ac-c902-46d1-b162-31a413244403 · outbound

This paper cites A study of the attention abnormality in trojaned berts.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation A study of the attention abnormality in trojaned berts

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:48.298891Z

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Observation f9be44d0-224e-415b-a2ba-9a2f986fab7b · outbound

This paper cites Attention-enhancing backdoor attacks against bert-based models.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Attention-enhancing backdoor attacks against bert-based models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:48.098956Z

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Observation 6cdf7015-e183-44cb-ae81-1a494a359f2f · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Communication-efficient learning of deep networks from decentralized data

Reference 38

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Observation 28b94390-7ad2-4c35-90f2-2ac8cd15a0a6 · outbound

This paper cites Asynchrony begets momentum, with an application to deep learning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Asynchrony begets momentum, with an application to deep learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:47.921860Z

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Observation 1c1c5d5c-d82b-4f2b-b674-9a1aa2265279 · outbound

This paper cites Training language models to follow instructions with human feedback.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Training language models to follow instructions with human feedback

Reference 40

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Observation e56aa170-0938-48af-8751-55c8663e5247 · outbound

This paper cites LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 41

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Observation 7b85753e-7e28-431a-bbb5-3bd28990abcd · outbound

This paper cites Sage- flow: Robust federated learning against both stragglers and adversaries.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Sage- flow: Robust federated learning against both stragglers and adversaries

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:47.767853Z

Source-reported events for the cited work

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Observation e11c79be-34da-404e-80c6-cdc27de70ccd · outbound

This paper cites Moment matching for multi-source domain adaptation.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Moment matching for multi-source domain adaptation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:47.602135Z

Source-reported events for the cited work

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Observation fac6a114-6957-4f53-98e7-d5fe47bf52ee · outbound

This paper cites Dynamicvit: Efficient vision transformers with dynamic token sparsification.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Dynamicvit: Efficient vision transformers with dynamic token sparsification

Reference 44

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Observation 88d9ffeb-151e-4723-988e-8673fdcdb947 · outbound

This paper cites Fedra: A random allocation strategy for federated tuning to unleash the power of heterogeneous clients.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fedra: A random allocation strategy for federated tuning to unleash the power of heterogeneous clients

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:47.436259Z

Source-reported events for the cited work

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Observation b7c4c915-30ea-46f0-a0f1-bc8402249bcb · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Improving loRA in privacy-preserving federated learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:47.224118Z

Source-reported events for the cited work

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Observation eeeda582-d280-493f-a1ef-1fe7d88c539d · outbound

This paper cites Glue: A multi-task benchmark and analysis platform for natural language understanding.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Glue: A multi-task benchmark and analysis platform for natural language understanding

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:47.063800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02ea8077-61fe-4838-af46-e392408e9382 · outbound

This paper cites {InfiniCache}: exploiting ephemeral serverless functions to build a {cost-effective} memory cache.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation {InfiniCache}: exploiting ephemeral serverless functions to build a {cost-effective} memory cache

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:46.903369Z

Source-reported events for the cited work

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Observation cbdfa344-a6c2-43bf-adcd-d721e78c8836 · outbound

This paper cites Super- naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Super- naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:46.754632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 12668488-4d7f-42af-905b-a5c7be40a40c · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:41.633323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97457e31-74ec-49d4-8e56-d1a0fbf33a50 · outbound

This paper cites Application of computerized adaptive testing to educational problems.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Application of computerized adaptive testing to educational problems

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:46.589306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 93833513-1718-44e8-a2cd-82f5a3a409c5 · outbound

This paper cites Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:41.770810Z

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Unavailable: canonical work link unavailable.

Observation ad32a674-9fb1-449b-9c1d-7e3f72fd7aab · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fedbiot: Llm local fine-tuning in federated learning without full model

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:46.409735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c7bc06c2-ee1e-436f-bed9-220bcb9e18da · outbound

This paper cites Fast-convergent federated learning with adaptive weighting.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fast-convergent federated learning with adaptive weighting

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:46.269703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8874bc94-2c6a-461a-a56c-dda6c9eabea1 · outbound

This paper cites Node selection toward faster convergence for federated learning on non-iid data.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Node selection toward faster convergence for federated learning on non-iid data

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:46.134420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8a4e72cb-df6b-4c44-b5dd-622cbcd48e6b · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fedfmsl: Federated learning of foundations models with sparsely activated lora

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:45.946960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 35c5e839-64bb-4ac0-a7bc-37bc7d5ccebb · outbound

This paper cites Fed2: Feature-aligned federated learning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fed2: Feature-aligned federated learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:45.801236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a78a566f-48b2-4eb5-842a-fd1e75ccb2a9 · outbound

This paper cites Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:42.293293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7754c9e1-045b-4e44-af86-0929c9ad6ace · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language- models.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language- models

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:45.588844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 22e2e385-6d76-4232-85e8-540d0b77cf7f · outbound

This paper cites Fedcust: Offloading hyperparameter cus- tomization for federated learning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fedcust: Offloading hyperparameter cus- tomization for federated learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:45.326010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 71633093-cbcb-4826-832b-5d49fa823ff2 · outbound

This paper cites Visualizing and understanding con- volutional networks.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Visualizing and understanding con- volutional networks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:45.095949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6dd677dc-bf52-4836-883c-f989e6a59ab1 · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Towards building the federatedgpt: Federated instruction tuning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:44.956641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c50bfd04-09db-490d-85c7-90cef79e14d2 · outbound

This paper cites Memory-adaptive depth-wise heterogenous federated learning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Memory-adaptive depth-wise heterogenous federated learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:44.764014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7af75b4b-73c9-45a8-b03d-5180bce8e416 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation OPT: Open Pre-trained Transformer Language Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:42.820717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3aabfc6-c58a-40c8-b729-ef47525cb67d · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:44.576929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fd7641be-d251-4a47-910f-b26acfb541cb · outbound

This paper cites Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-07T01:03:43.998895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e051bc65-a625-4a25-a0db-c6cdf91ce64a · outbound

This paper cites Fedprompt: Communication-efficient and privacy-preserving prompt tuning in federated learning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Fedprompt: Communication-efficient and privacy-preserving prompt tuning in federated learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:03:44.397325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d88c3471-04c2-46a4-a9d7-62b3a551f277 · outbound

This paper cites A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:43.195312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cb11d54-66ce-4ceb-87de-62b0a69e16f2 · outbound

This paper cites AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:43.253839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04a379ed-b54d-492a-98b5-36f02eaa89f0 · outbound

This paper cites Exact Penalty Method for Federated Learning.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Exact Penalty Method for Federated Learning

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-07T01:03:43.716682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bc7a9714-61d7-43b3-9f4a-9c57183a0624 · outbound

This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:43.439595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73dc3062-6bc7-4978-8d56-d9eafedb44fa · outbound

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

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:43.510839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 09d5f3f0-c7d0-48d1-8492-ef49dae1ca23 · inbound

Heterogeneity-Oblivious Robust Federated Learning cites this paper.

Heterogeneity-Oblivious Robust Federated Learning Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T04:27:20.977277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab99250-ab6f-4360-a65b-314433ffc06c · inbound

Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs cites this paper.

Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:01:21.162053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.