Paper Citation Record · LEDGER

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning

As of 20 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.01001.

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

pith.paper-citation-record.v1
2506.01001 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:58:00.182810Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact2
verified fuzzy14
unresolved24
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 41de330c-98ab-4a01-9970-9be5add0feda · outbound

This paper cites GPT-4 Technical Report.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning GPT-4 Technical Report

Reference 1

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Observation f62451c4-8964-4916-871f-16179796ab8c · outbound

This paper cites DeepSeek-V3 Technical Report.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning DeepSeek-V3 Technical Report

Reference 2

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Observation 0f612bfa-093f-4005-be41-c31df01c9119 · outbound

This paper cites How can we know what language models know?.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning How can we know what language models know?

Reference 3

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Observation 3d752210-7274-478a-9cc0-4b386ecdfb26 · outbound

This paper cites Sentiment Analysis in the Era of Large Language Models: A Reality Check.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Sentiment Analysis in the Era of Large Language Models: A Reality Check

Reference 4

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This paper cites Prompting large language model for machine translation: A case study,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Prompting large language model for machine translation: A case study,

Reference 5

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Observation bf1708c9-d6c1-4025-9cbe-925309f3df0e · outbound

This paper cites Keystrokesniffer: An off-the-shelf smart- phone can eavesdrop on your privacy from anywhere,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Keystrokesniffer: An off-the-shelf smart- phone can eavesdrop on your privacy from anywhere,

Reference 6

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Observation 6657ddce-6bb1-4600-a7ff-bb344ead3138 · outbound

This paper cites The eu general data protec- tion regulation (gdpr),.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning The eu general data protec- tion regulation (gdpr),

Reference 7

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Observation c5dcf045-2681-4a11-bed9-37e128fe7602 · outbound

This paper cites FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

Reference 8

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Observation da1e5f4e-3446-4e5e-bc45-25f224c4f47d · outbound

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

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Fedpetuning: When federated learning meets the parameter- efficient tuning methods of pre-trained language models,

Reference 9

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Observation bccc9c53-af6d-4b87-9181-9373a8620202 · outbound

This paper cites Language models are unsupervised multitask learners,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Language models are unsupervised multitask learners,

Reference 10

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Observation e915c4db-ea7e-4629-b60b-efff5a1d0136 · outbound

This paper cites A survey on optimized implementation of deep learn- ing models on the nvidia jetson platform,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning A survey on optimized implementation of deep learn- ing models on the nvidia jetson platform,

Reference 11

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Source-reported events for the cited work

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Observation cac2410b-caa7-442b-90dc-0d1e141ad889 · outbound

This paper cites Finch: Enhancing federated learning with hierarchical neural architecture search,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Finch: Enhancing federated learning with hierarchical neural architecture search,

Reference 12

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Observation a491e300-2bd8-441a-bf3d-bb94fca54438 · outbound

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

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning FedAdapter: Efficient Federated Learning for Modern NLP

Reference 13

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Observation f83677b1-9ae5-4e56-aa36-05be962fd5ca · outbound

This paper cites How much ram does your android phone really need in 2025?.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning How much ram does your android phone really need in 2025?

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:03.718159Z

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Observation d4e67ab8-ec73-4c53-92dd-c722371bbc5c · outbound

This paper cites Parameter- efficient transfer learning for nlp,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Parameter- efficient transfer learning for nlp,

Reference 15

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Source-reported events for the cited work

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Observation 2e988e23-cbc1-4178-befd-4321c9934bd4 · outbound

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

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 16

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Observation ed04c89b-5041-40ab-bd06-e3c901715fb1 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 17

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Observation 92b55702-ce45-4310-9d10-925cfb3c2581 · outbound

This paper cites Adaptive local update and neural composition for accelerating federated learning in heterogeneous edge networks,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Adaptive local update and neural composition for accelerating federated learning in heterogeneous edge networks,

Reference 18

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Observation 19dfc9cf-c395-4ce6-a61d-333cbcc49a5d · outbound

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

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Fedra: A random allocation strategy for federated tuning to unleash the power of heterogeneous clients,

Reference 19

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Source-reported events for the cited work

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Observation 22b7e0f0-0fdb-435b-98c9-aafcaa58a389 · outbound

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

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning No one left behind: Inclusive federated learning over heterogeneous devices,

Reference 20

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Observation c8422aad-8b57-49da-b1a8-df0355384246 · outbound

This paper cites DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation

Reference 21

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Observation ecfd8196-2c17-4af6-904e-ed2e20d092ec · outbound

This paper cites Exploring Selective Layer Fine-Tuning in Federated Learning.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Exploring Selective Layer Fine-Tuning in Federated Learning

Reference 22

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Observation b280aeb0-cd08-47e3-b9a6-932310484479 · outbound

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

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Het- erogeneous lora for federated fine-tuning of on-device foundation models,

Reference 23

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Observation 87162867-1e65-4d4d-a9d5-441b0f1c9c57 · outbound

This paper cites A stochastic approximation method,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning A stochastic approximation method,

Reference 24

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Observation 849d16ff-8d41-4db8-b56c-5008fb0470bf · outbound

This paper cites Fedlora: When personalized federated learning meets low-rank adapta- tion,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Fedlora: When personalized federated learning meets low-rank adapta- tion,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:02.741146Z

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Observation 5114f726-5d64-4e7f-bd1b-4f904a0cc253 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 26

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This paper cites Triton: an intermediate language and compiler for tiled neural network computations,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Triton: an intermediate language and compiler for tiled neural network computations,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:02.556222Z

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Observation e1ab9eda-4c5c-49b5-bd38-1aaacba4c903 · outbound

This paper cites Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization

Reference 28

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Observation 3299f904-27dd-41d9-933c-fc372f8a9bdd · outbound

This paper cites Adaptive control of local updating and model compression for efficient federated learning,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Adaptive control of local updating and model compression for efficient federated learning,

Reference 29

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Observation 0428004e-f3d0-41ad-bc04-d905a76844df · outbound

This paper cites Gora: Gradient-driven adaptive low rank adaptation,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Gora: Gradient-driven adaptive low rank adaptation,

Reference 30

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FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Transformers library,

Reference 31

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Observation b200752b-8078-4d93-85a4-e735a4698be3 · outbound

This paper cites SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics

Reference 32

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This paper cites Pytorch: An im- perative style, high-performance deep learning library,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Pytorch: An im- perative style, high-performance deep learning library,

Reference 33

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FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 34

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This paper cites DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

Reference 35

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This paper cites Pre-trained weights for llms,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Pre-trained weights for llms,

Reference 36

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Observation 790059bb-d41c-413a-b7ed-fed7c83f940c · outbound

This paper cites {FwdLLM}: Efficient federated finetuning of large language models with perturbed inferences,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning {FwdLLM}: Efficient federated finetuning of large language models with perturbed inferences,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:01.494256Z

Source-reported events for the cited work

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Observation e2c4997b-a703-436c-8b5a-8182a4807728 · outbound

This paper cites Enhancing semi-supervised federated learning with progressive training in heterogeneous edge computing,.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Enhancing semi-supervised federated learning with progressive training in heterogeneous edge computing,

Reference 38

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Source-reported events for the cited work

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Observation a82d5952-4738-4eb2-956f-17526c933222 · outbound

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

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models

Reference 39

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Source-reported events for the cited work

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Observation 21999485-a5ad-4162-ade6-09f39cdbbffa · outbound

This paper cites Adam: A Method for Stochastic Optimization.

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning Adam: A Method for Stochastic Optimization

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:58:00.182810Z

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Pith citing papers

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