Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T11:23:55.319936Z
Paper Citation Record · LEDGER
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks
As of 13 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2412.00090.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
14 of 14 outbound references displayed
- verified exact1
- verified fuzzy10
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b7f5865e-4839-4b9c-9f29-3fe89cb5b4bb · outbound
This paper cites Large language model-based wireless network design,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks Large language model-based wireless network design,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:23:55.618699Z
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 a8332933-34df-4c27-b6aa-0ab52ca100f3 · outbound
This paper cites LLM-based edge intelligence: A compreh ensive survey on architectures, applications, security and trust worthiness,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks LLM-based edge intelligence: A compreh ensive survey on architectures, applications, security and trust worthiness,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:23:55.608245Z
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 93d003db-780d-4c56-81ab-04b6a0d70e84 · outbound
This paper cites Gradient-based parameter selection for efficien t fine-tuning,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks Gradient-based parameter selection for efficien t fine-tuning,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:23:55.597324Z
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 cd74da69-1916-4050-bd8f-d538cbc8df74 · outbound
This paper cites Knowledge-d riven deep learning paradigms for wireless network optimization in 6G,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks Knowledge-d riven deep learning paradigms for wireless network optimization in 6G,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:23:55.586444Z
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 bc2b38a0-2e0c-40d6-a9a7-f103cc4ca89b · outbound
This paper cites To talk or to work: Flexible communication compression for energy efficient fe derated learn- ing over heterogeneous mobile edge devices,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks To talk or to work: Flexible communication compression for energy efficient fe derated learn- ing over heterogeneous mobile edge devices,
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:23:55.285923Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9a796f5a-9dba-4209-ad31-dbe7683ebfd1 · outbound
This paper cites ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:23:55.289702Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d15a0343-0c9b-4fe3-8c0a-0b8ac091ca6c · outbound
This paper cites Plut o and Charon: A time and memory efficient collaborative edge AI fra mework for personal LLMs fine-tuning,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks Plut o and Charon: A time and memory efficient collaborative edge AI fra mework for personal LLMs fine-tuning,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:23:55.574638Z
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 b1257ea0-928d-466f-8e5e-cf25bc40f2b3 · outbound
This paper cites Split learning over wireless networks: Parallel design an d resource management,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks Split learning over wireless networks: Parallel design an d resource management,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:23:55.561890Z
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 df75af74-cc6d-4475-836f-1a2e5541d4c1 · outbound
This paper cites Device-edge cooperative fin e-tuning of foundation models as a 6G service,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks Device-edge cooperative fin e-tuning of foundation models as a 6G service,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:23:55.549906Z
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 f82a7989-9751-4d17-a772-8a0c6507f572 · outbound
This paper cites Resource allocation for stable LLM t raining in mobile edge computing,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks Resource allocation for stable LLM t raining in mobile edge computing,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:23:55.537590Z
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 a11450f7-80a4-48dc-bb64-a36e357d9ffd · outbound
This paper cites Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T11:23:55.366703Z
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 fa5e7a0e-4d8b-4a0b-9353-86036c138e66 · outbound
This paper cites NR; Physical layer procedures for data,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks NR; Physical layer procedures for data,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:23:55.525363Z
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 85afcb24-ef0d-4d61-b09b-1775a8601b25 · outbound
This paper cites D elay-aware microservice coordination in mobile edge computing: A rein forcement learning approach,.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks D elay-aware microservice coordination in mobile edge computing: A rein forcement learning approach,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:23:55.511871Z
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 098e02f0-4c91-4158-8f43-c92dbf7fffe2 · outbound
This paper cites SpinQuant: LLM quantization with learned rotations.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks SpinQuant: LLM quantization with learned rotations
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:23:55.319936Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.