Paper Citation Record · LEDGER

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

As of 7 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2505.24773.

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

pith.paper-citation-record.v1
2505.24773 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:20:48.076697Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T05:03:25.152278Z

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 fuzzy7
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5cfaf0b7-903e-4b77-a402-b973a5cb1be8 · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Training language models to follow instructions with human feedback,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.277993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7059cc64-17e0-442c-8c83-4a87fb4c262a · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Finetuned Language Models Are Zero-Shot Learners

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.330283Z

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Observation 69d27e8f-8e14-4982-9e48-acb16023680c · outbound

This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.378189Z

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Unavailable: canonical work link unavailable.

Observation 32486633-96cf-4c92-806f-69607f8a495a · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Federated Learning: Strategies for Improving Communication Efficiency

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.453259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 387ac1d5-687b-4c7e-a857-18a45be4184c · outbound

This paper cites Federated learning: Challenges, methods, and future directions,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Federated learning: Challenges, methods, and future directions,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.548062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae831fcf-df98-4a7a-9cbb-153ee3cd4eb3 · outbound

This paper cites Federated learning review: Fundamentals, enabling technologies, and future applications,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Federated learning review: Fundamentals, enabling technologies, and future applications,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.611485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa64dccf-cba7-4826-8d8f-b43546c57993 · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Parameter-efficient transfer learning for nlp,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.651157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 038e36f7-6676-4b6b-9391-e7d85dd8580c · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Bitfit: Simple parameter- efficient fine-tuning for transformer-based masked language-models,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.727750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7adf39c7-3a3d-4436-a592-86e4662a2f89 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.806920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b4a9491-9019-464b-be06-5e6168d62e29 · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Lora: Low-rank adaptation of large language models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.870213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a76ac7c-92e9-407d-9c91-fad565449d08 · outbound

This paper cites LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.912837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8002dc84-f92f-4bfd-928b-f3a3a3ba5c8c · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:45.975885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22785ce4-23a8-4781-9026-58233479f3fb · outbound

This paper cites Initialization using update approximation is a silver bullet for extremely efficient low-rank fine-tuning,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Initialization using update approximation is a silver bullet for extremely efficient low-rank fine-tuning,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:46.052950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb169f4e-89fe-4ed7-80c4-fe6e81a58e12 · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:46.142862Z

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Unavailable: canonical work link unavailable.

Observation 3cc42069-44e4-4dab-b979-d1cc4cae243a · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption FedAdapter: Efficient Federated Learning for Modern NLP

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:46.186692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bcc6d0d-2415-4dcb-9dd1-cc95ee8f7b9a · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Fedbiot: Llm local fine-tuning in federated learning without full model,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:46.295213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92e1ba78-aca2-4050-96d2-212c1c6e74e4 · outbound

This paper cites Improving LoRA in Privacy-preserving Federated Learning.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Improving LoRA in Privacy-preserving Federated Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:46.403337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6e9d5cc-ccfd-4cb5-b568-324bf49761b2 · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Federated fine-tuning of large language models under heterogeneous language tasks and client resources,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:50.061507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e02cad9-dde7-4802-92d8-9f18a4c15c5f · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:46.494096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e896afe7-f93e-4da7-95a8-d999777ad23d · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Towards building the federatedgpt: Federated instruction tun- ing,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:46.603877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 144064f0-3f73-4ada-a6a0-373958864287 · outbound

This paper cites pfedprompt: Learning personalized prompt for vision-language models in federated learning,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption pfedprompt: Learning personalized prompt for vision-language models in federated learning,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:46.705973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3789c63-fed6-417c-bca5-8a70cfc3488d · outbound

This paper cites Fedperfix: To- wards partial model personalization of vision transformers in federated learning,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Fedperfix: To- wards partial model personalization of vision transformers in federated learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:49.820289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9f10df48-f1db-4917-8bb5-50ed961a997b · outbound

This paper cites Openfedllm: Training large language models on decentralized private data via federated learning,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Openfedllm: Training large language models on decentralized private data via federated learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:49.613704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0ae61987-dcfd-4266-8fc4-50f81099f13d · outbound

This paper cites Fed-sb: A silver bullet for extreme communication efficiency and performance in (private) federated lora fine-tuning,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Fed-sb: A silver bullet for extreme communication efficiency and performance in (private) federated lora fine-tuning,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:46.927971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 381c6472-6ee2-4755-8454-4ed85d0fd23b · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Selective Aggregation for Low-Rank Adaptation in Federated Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.004308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 506ca283-86eb-48b1-aa66-e39b610883ef · outbound

This paper cites Fedlfc: Towards efficient federated multilingual modeling with lora-based language family clustering,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Fedlfc: Towards efficient federated multilingual modeling with lora-based language family clustering,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:49.461657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e353da8-2687-4e40-aa95-c5e973d87ec6 · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.108294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18c1f2a7-74a8-475e-8acf-6148069d0b82 · outbound

This paper cites Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:20:48.468025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9098f3c6-2c81-4b34-9799-affbeedddb4b · outbound

This paper cites AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:20:48.359240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 097f21ec-631e-4809-b37d-5bc2c1b68d60 · outbound

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

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Communication-efficient learning of deep networks from decentralized data,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.328672Z

Source-reported events for the cited work

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Observation 5ade2baa-a2ee-414c-b221-c5b380210b51 · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.363535Z

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Unavailable: canonical work link unavailable.

Observation 7810aaf4-1a9a-47d7-9515-7d5fe1210573 · outbound

This paper cites FinGPT: Democratizing Internet-scale Data for Financial Large Language Models.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption FinGPT: Democratizing Internet-scale Data for Financial Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.461323Z

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Observation 3e4f6fd4-16ae-4c48-9db9-108fb0a11180 · outbound

This paper cites Conover, M.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Conover, M

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:49.331729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f3f68a7a-1996-471d-bc9b-d96702a25c74 · outbound

This paper cites Character-level convolutional net- works for text classification,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Character-level convolutional net- works for text classification,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:49.231877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 87fcd8d5-4729-44a7-91e6-afeddd1f54aa · outbound

This paper cites Stanford alpaca: An instruction-following llama model,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Stanford alpaca: An instruction-following llama model,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.708092Z

Source-reported events for the cited work

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Observation a0f0cdd7-e52a-415e-b568-339a1d9781d3 · outbound

This paper cites Language models are unsupervised multitask learners,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Language models are unsupervised multitask learners,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.805559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09e11dc5-3d70-4942-933e-43b0984ab384 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption TinyLlama: An Open-Source Small Language Model

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.871962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 846a9050-78b5-46ef-94f3-6aa245c7bce2 · outbound

This paper cites Qwen3 Technical Report.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Qwen3 Technical Report

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.910435Z

Source-reported events for the cited work

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Observation 49d3ce46-8317-4109-b4de-998d8fd56604 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Measuring Massive Multitask Language Understanding

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.981388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2182f193-9768-4fab-bc83-23e5fb1a5705 · outbound

This paper cites Good debt or bad debt: Detecting semantic orientations in economic texts,.

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption Good debt or bad debt: Detecting semantic orientations in economic texts,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:49.093252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation c110d243-9905-40b3-a029-e4f09786feb6 · inbound

Attention-Free and Lightweight Token Reduction for Efficient Vision-Language Models cites this paper.

Attention-Free and Lightweight Token Reduction for Efficient Vision-Language Models AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T05:03:25.152278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.