Paper Citation Record · LEDGER

Split Fine-Tuning for Large Language Models in Wireless Networks

As of 14 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2501.09237.

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

pith.paper-citation-record.v1
2501.09237 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:12:57.603125Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

42 of 42 outbound references displayed

verified exact0
verified fuzzy34
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a1f66ed5-9162-46ae-b559-a0faf73749eb · outbound

This paper cites Efficient federated learning for modern NLP,.

Split Fine-Tuning for Large Language Models in Wireless Networks Efficient federated learning for modern NLP,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.854661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 39750c94-97a4-476c-b659-6105a1e2ce0e · outbound

This paper cites DeViT: Decomposing vision transformers for collaborative inference in edge devices,.

Split Fine-Tuning for Large Language Models in Wireless Networks DeViT: Decomposing vision transformers for collaborative inference in edge devices,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.826686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9db0cf7d-43ed-409e-9c3e-d385e979be07 · outbound

This paper cites Dual vision transformer,.

Split Fine-Tuning for Large Language Models in Wireless Networks Dual vision transformer,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.803499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9807e3ae-b83c-4526-ba45-d81a69e6ee4b · outbound

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

Split Fine-Tuning for Large Language Models in Wireless Networks Federatedscope-LLM: A comprehensive package for fine-tuning large language models in federated learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.780507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5e528cc9-e12c-44e3-9233-8b3ece771cee · outbound

This paper cites Holistic network virtualization and pervasive network intelligence for 6G,.

Split Fine-Tuning for Large Language Models in Wireless Networks Holistic network virtualization and pervasive network intelligence for 6G,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.756204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 18fc63fe-5bed-4024-9b0d-b8279df76e1b · outbound

This paper cites WirelessLLM: Empowering large language models towards wireless intelligence,.

Split Fine-Tuning for Large Language Models in Wireless Networks WirelessLLM: Empowering large language models towards wireless intelligence,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.734234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0ec631d5-00a3-49c0-9e6b-e2500a788172 · outbound

This paper cites LLM-powered synthetic environments for self- driving scenarios,.

Split Fine-Tuning for Large Language Models in Wireless Networks LLM-powered synthetic environments for self- driving scenarios,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.712856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a56d0d22-ba8a-4dfc-b4db-21336678b1d3 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation,.

Split Fine-Tuning for Large Language Models in Wireless Networks Prefix-tuning: Optimizing continuous prompts for generation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.687145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9db67a9f-9d41-4de8-a8a9-6dda4185e908 · outbound

This paper cites AI-assisted network-slicing based next-generation wireless networks,.

Split Fine-Tuning for Large Language Models in Wireless Networks AI-assisted network-slicing based next-generation wireless networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.663976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 30f034ac-82b8-4e8e-9c55-9b1a8bd81fb2 · outbound

This paper cites Efficient and privacy-preserving feature importance-based vertical federated learning,.

Split Fine-Tuning for Large Language Models in Wireless Networks Efficient and privacy-preserving feature importance-based vertical federated learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.624227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7dd6d407-7295-4b58-9e6e-838883cc395c · outbound

This paper cites FL-TAC: Enhanced fine-tuning in federated learning via low-rank, task-specific adapter clustering,.

Split Fine-Tuning for Large Language Models in Wireless Networks FL-TAC: Enhanced fine-tuning in federated learning via low-rank, task-specific adapter clustering,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.598533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5d03b431-76cd-4123-ac47-6aec64031f10 · outbound

This paper cites AI-native network slicing for 6G networks,.

Split Fine-Tuning for Large Language Models in Wireless Networks AI-native network slicing for 6G networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.576324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8821ef0a-b7a9-49e2-9dc2-975d8622751c · outbound

This paper cites Unstructured pruning and low rank factori- sation of self-supervised pre-trained speech models,.

Split Fine-Tuning for Large Language Models in Wireless Networks Unstructured pruning and low rank factori- sation of self-supervised pre-trained speech models,

Reference 13

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T20:12:57.968560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c57dfa29-dcab-4967-bf23-acb4c963dce0 · outbound

This paper cites Digital twin based user-centric resource management for multicast short video streaming,.

Split Fine-Tuning for Large Language Models in Wireless Networks Digital twin based user-centric resource management for multicast short video streaming,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T20:12:57.403118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ac228b3-c656-4620-9afd-4399f755871e · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Split Fine-Tuning for Large Language Models in Wireless Networks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T20:12:57.411279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fd80d43-d18d-4baa-80f5-a82577abbdde · outbound

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

Split Fine-Tuning for Large Language Models in Wireless Networks Parameter-efficient transfer learning for NLP,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.535765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 190294eb-c6db-4a11-9d52-e7a8e4e3a0dc · outbound

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

Split Fine-Tuning for Large Language Models in Wireless Networks The power of scale for parameter-efficient prompt tuning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.509094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c9e30f95-9456-45cf-9141-8710117020a6 · outbound

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

Split Fine-Tuning for Large Language Models in Wireless Networks Towards a unified view of parameter-efficient transfer learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.487919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c9acd922-9673-4f38-9675-18c79caae8f0 · outbound

This paper cites FedMes: Speeding up federated learning with multiple edge servers,.

Split Fine-Tuning for Large Language Models in Wireless Networks FedMes: Speeding up federated learning with multiple edge servers,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.465122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4e9253bb-ed2f-43ea-9f6b-c61dd481c0f9 · outbound

This paper cites DetFed: Dynamic resource scheduling for deterministic federated learning over time-sensitive networks,.

Split Fine-Tuning for Large Language Models in Wireless Networks DetFed: Dynamic resource scheduling for deterministic federated learning over time-sensitive networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.438288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c156baff-c34d-4114-8acb-20bfe473a217 · outbound

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

Split Fine-Tuning for Large Language Models in Wireless Networks PromptFL: Let federated participants cooperatively learn prompts instead of models – federated learning in age of foundation model,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.415624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cb5e96ac-276d-4196-b96c-4502f5d0d487 · outbound

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

Split Fine-Tuning for Large Language Models in Wireless Networks Low-parameter federated learning with large language models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.391526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a705eeb7-cd16-4f70-bf77-6d71d71f532d · outbound

This paper cites Efficient federated learning for modern NLP,.

Split Fine-Tuning for Large Language Models in Wireless Networks Efficient federated learning for modern NLP,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.364612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 58a124e5-e506-43ea-9342-41ee58d95be1 · outbound

This paper cites En- semble distillation based adaptive quantization for supporting federated learning in wireless networks,.

Split Fine-Tuning for Large Language Models in Wireless Networks En- semble distillation based adaptive quantization for supporting federated learning in wireless networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.338438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9f16912f-47a0-42fe-a4ac-1e3e589379c8 · outbound

This paper cites Sparse training for federated learning with regularized error correction,.

Split Fine-Tuning for Large Language Models in Wireless Networks Sparse training for federated learning with regularized error correction,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.316975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 652b4ada-b4e5-418b-a379-2cbb8c8d57f8 · outbound

This paper cites Split federated learning: Speed up model training in resource-limited wireless networks,.

Split Fine-Tuning for Large Language Models in Wireless Networks Split federated learning: Speed up model training in resource-limited wireless networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.294932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3f6916ac-88b1-4cb6-b3dd-3ee624ff7c27 · outbound

This paper cites Split learning over wireless networks: Parallel design and resource management,.

Split Fine-Tuning for Large Language Models in Wireless Networks Split learning over wireless networks: Parallel design and resource management,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T20:12:57.496492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73236b9d-b441-4626-9905-4beb58b0e381 · outbound

This paper cites Accelerating split federated learning over wireless communication networks,.

Split Fine-Tuning for Large Language Models in Wireless Networks Accelerating split federated learning over wireless communication networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.262019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6c38258c-d54e-490c-958a-1b6e597dbc02 · outbound

This paper cites Accelerating federated learning with data and model parallelism in edge computing,.

Split Fine-Tuning for Large Language Models in Wireless Networks Accelerating federated learning with data and model parallelism in edge computing,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.242842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c5053aa3-9d90-417a-96cd-c763a4dd15b4 · outbound

This paper cites ParallelSFL: A novel split federated learning framework tackling heterogeneity issues,.

Split Fine-Tuning for Large Language Models in Wireless Networks ParallelSFL: A novel split federated learning framework tackling heterogeneity issues,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.221877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2045b0f5-04bc-4faa-b81c-11021299e512 · outbound

This paper cites Device-edge cooperative fine-tuning of foundation models as a 6G service,.

Split Fine-Tuning for Large Language Models in Wireless Networks Device-edge cooperative fine-tuning of foundation models as a 6G service,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.194741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ac84b2f9-fca4-4f01-be23-618fc2505101 · outbound

This paper cites Pluto and Charon: A time and memory efficient collaborative edge AI framework for personal LLMs fine-tuning,.

Split Fine-Tuning for Large Language Models in Wireless Networks Pluto and Charon: A time and memory efficient collaborative edge AI framework for personal LLMs fine-tuning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.171349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7edae3d2-c2e6-4907-a19e-1fc9014fada8 · outbound

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

Split Fine-Tuning for Large Language Models in Wireless Networks Improving LoRA in Privacy-preserving Federated Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T20:12:57.536855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebf21ea2-fdd3-4232-8706-4ab682396b9b · outbound

This paper cites Fast: Fidelity-adjustable semantic transmission over heterogeneous wireless networks,.

Split Fine-Tuning for Large Language Models in Wireless Networks Fast: Fidelity-adjustable semantic transmission over heterogeneous wireless networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.152274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2479eaca-97ac-4a0b-ad45-718b99773d18 · outbound

This paper cites Robust and communication-efficient federated learning from non-IID data,.

Split Fine-Tuning for Large Language Models in Wireless Networks Robust and communication-efficient federated learning from non-IID data,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.130545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 93447d9b-7340-47b7-980e-9707fc683633 · outbound

This paper cites Run-length encodings (corresp.),.

Split Fine-Tuning for Large Language Models in Wireless Networks Run-length encodings (corresp.),

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.106929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation af8d3341-3a80-4071-85df-449afebe51f1 · outbound

This paper cites Adap- tive digital twin-assisted 3C management for QoE-driven MSVS: A GAI-based DRL approach,.

Split Fine-Tuning for Large Language Models in Wireless Networks Adap- tive digital twin-assisted 3C management for QoE-driven MSVS: A GAI-based DRL approach,

Reference 37

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T20:12:57.776037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cc928289-5640-43eb-adc5-c664b103e488 · outbound

This paper cites Resource allocation for stable LLM training in mobile edge computing,.

Split Fine-Tuning for Large Language Models in Wireless Networks Resource allocation for stable LLM training in mobile edge computing,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.077719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b8cc0d8b-3753-4581-872b-149b44ecb007 · outbound

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

Split Fine-Tuning for Large Language Models in Wireless Networks Learning multiple layers of features from tiny images,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T20:12:57.578413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 472e479a-1864-47be-8d47-143cf02af34b · outbound

This paper cites FedGKD: Toward heterogeneous federated learning via global knowledge distillation,.

Split Fine-Tuning for Large Language Models in Wireless Networks FedGKD: Toward heterogeneous federated learning via global knowledge distillation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:58.045766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c398b677-a499-40a7-9b85-5c9192f673f9 · outbound

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

Split Fine-Tuning for Large Language Models in Wireless Networks Communication-efficient learning of deep networks from decentralized data,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T20:12:57.593118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2000854f-5e94-46df-83b0-853cb47c394a · outbound

This paper cites Distributed learning of deep neural network over multiple agents,.

Split Fine-Tuning for Large Language Models in Wireless Networks Distributed learning of deep neural network over multiple agents,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:57.996893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.