Paper Citation Record · LEDGER

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence

As of 5 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2605.26120.

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

pith.paper-citation-record.v1
2605.26120 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-05T08:00:17.200577Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

35 of 35 outbound references displayed

verified exact8
verified fuzzy27
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 513e74b1-7811-4815-a4cb-75d5572254ff · outbound

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

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.026092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 99a10131-79fd-4732-8e01-e74a78c28b4f · outbound

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

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-05T08:00:46.974283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0acd9483-036a-4fed-b752-e86531ae1ccd · outbound

This paper cites GPT-4 Technical Report.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence GPT-4 Technical Report

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-05T08:00:46.969234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 060ce938-9722-48de-b84d-75107e23a8d5 · outbound

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

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Federated fine-tuning of large language models under heterogeneous tasks and client resources,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.019128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8bdae7b0-0376-438e-bcdf-188df953e1fe · outbound

This paper cites SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-05T08:00:46.972623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 213f4052-75bf-4f39-82c9-754899c898c6 · outbound

This paper cites Mobile edge intelligence for large language models: A contemporary survey,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Mobile edge intelligence for large language models: A contemporary survey,

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-05T08:00:46.950078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 641e6368-96cb-47e8-a731-39459c49d59d · outbound

This paper cites Efficient federated learning for modern nlp,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Efficient federated learning for modern nlp,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.038635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bbed4852-6233-4532-ab37-ccc1a68df072 · outbound

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

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Communication-efficient learning of deep networks from decentralized data,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.009084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 90cf531e-8f08-4ca4-9c05-76aa4276c82b · outbound

This paper cites Mobilora: Accelerating lora-based llm inference on mobile devices via context-aware kv cache optimization,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Mobilora: Accelerating lora-based llm inference on mobile devices via context-aware kv cache optimization,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.014448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7c13de6e-892f-45c8-8347-9e0c46556fbd · outbound

This paper cites Split learning for health: Distributed deep learning without sharing raw patient data.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Split learning for health: Distributed deep learning without sharing raw patient data

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-05T08:00:46.977183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f3c5cd18-5d44-481e-b514-8b2c15faea63 · outbound

This paper cites Splitfed: When federated learning meets split learning,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Splitfed: When federated learning meets split learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.023671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f82d3883-1f53-4aa0-b870-b03d49c00241 · outbound

This paper cites Split federated learning empowered vehicular edge intelligence: Concept, adaptive de- sign, and future directions,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Split federated learning empowered vehicular edge intelligence: Concept, adaptive de- sign, and future directions,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.030612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ab3af8af-ecc5-4bc8-8a18-e7d58f7e41c9 · outbound

This paper cites Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-05T08:00:46.962844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c9de9c02-a2ba-4eb9-922b-fba1b002bcff · outbound

This paper cites Quantized federated learning under transmission delay and outage constraints,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Quantized federated learning under transmission delay and outage constraints,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.032875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ae312017-4dac-4924-9f7d-dbf2818a28e0 · outbound

This paper cites Communication-efficient federated learning: A variance-reduced stochastic approach with adaptive sparsification,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Communication-efficient federated learning: A variance-reduced stochastic approach with adaptive sparsification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.029680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5bc000a7-f8c4-460a-ba3e-83bf9dbcc9e2 · outbound

This paper cites On the road to 6G: Visions, requirements, key technologies, and testbeds,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence On the road to 6G: Visions, requirements, key technologies, and testbeds,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.011324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9dd59ae4-4705-4890-947e-c4b8b7a2b5c4 · outbound

This paper cites Not all tokens are equal: Human-centric visual analysis via token clustering transformer,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Not all tokens are equal: Human-centric visual analysis via token clustering transformer,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.035197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 851063b1-113b-4d8c-9130-a35c24de135c · outbound

This paper cites Wireless distributed learning: A new hybrid split and federated learning approach,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Wireless distributed learning: A new hybrid split and federated learning approach,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.021685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6ccd3897-3f5c-4a7f-a3b4-5be09758d228 · outbound

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

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Split learning over wireless networks: Parallel design and resource management,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.001244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5a403cf2-8024-4ccf-8cdf-533a91372d40 · outbound

This paper cites Adaptive and parallel split federated learning in vehicular edge computing,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Adaptive and parallel split federated learning in vehicular edge computing,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.005376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ce6e37b2-f321-419c-ae0f-b16635ca1526 · outbound

This paper cites SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-05T08:00:46.976029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 96370014-2b36-4442-b5fe-d16cac66d564 · outbound

This paper cites Design and analysis of uplink and downlink communications for federated learning,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Design and analysis of uplink and downlink communications for federated learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.003363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bb3e08ce-fea3-425a-bdcb-ea599f52aa93 · outbound

This paper cites Communication-efficient federated learning for heterogeneous edge devices based on adaptive gradient quantization,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Communication-efficient federated learning for heterogeneous edge devices based on adaptive gradient quantization,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:46.989612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1a173448-1d18-4e05-87e4-f3946a4d88f4 · outbound

This paper cites Service delay minimization for federated learning over mobile devices,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Service delay minimization for federated learning over mobile devices,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:46.994182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0238ca8e-0bbc-4460-9c75-f689fd7a6661 · outbound

This paper cites Fed-cvlc: Compressing federated learning communications with variable-length codes,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Fed-cvlc: Compressing federated learning communications with variable-length codes,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.027588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a4961fb3-b77f-429e-bd2a-92bd729f3409 · outbound

This paper cites Joint accuracy and latency optimization for quantized federated learning in vehicular networks,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Joint accuracy and latency optimization for quantized federated learning in vehicular networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.025880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b77d1647-f717-4c7b-a63e-3fe57126a378 · outbound

This paper cites Joint gradient sparsification and device scheduling for federated learning,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Joint gradient sparsification and device scheduling for federated learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:46.999204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6f6b22e0-f9b1-4f07-b367-21efd59ca650 · outbound

This paper cites Federated split learning with model pruning and gradient quantization in wireless networks,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Federated split learning with model pruning and gradient quantization in wireless networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.003894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a830506f-5235-4890-8fb7-9f29c13453cc · outbound

This paper cites Reducing Communication for Split Learning by Randomized Top-k Sparsification.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Reducing Communication for Split Learning by Randomized Top-k Sparsification

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-07-05T08:00:46.967505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 75b473f9-ba00-4515-8c93-a4e1b3bad954 · outbound

This paper cites Adaptive resource allocation for semantic communication networks,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Adaptive resource allocation for semantic communication networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.001778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dc512f3e-f20d-446e-a7e9-e9a879b77ebc · outbound

This paper cites Energy-efficient federated edge learning with streaming data: A lyapunov optimization approach,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Energy-efficient federated edge learning with streaming data: A lyapunov optimization approach,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.028413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a418d397-195d-4111-ac6a-7d8b30498aea · outbound

This paper cites Aigc-assisted federated learning for vehicular edge intelligence: Vehicle selection, resource allocation and model augmentation,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Aigc-assisted federated learning for vehicular edge intelligence: Vehicle selection, resource allocation and model augmentation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.016703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d42d61f5-29a0-4f7b-99e3-b74e2f892b77 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Imagenet large scale visual recognition challenge,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.033465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5386ebe1-73ad-4fe5-a2cb-0cd22ada454b · outbound

This paper cites Automated flower classification over a large number of classes,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence Automated flower classification over a large number of classes,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:46.983659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7ca4a34e-493b-412b-8bdd-d70f0e802ed7 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset,.

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence The caltech-ucsd birds-200-2011 dataset,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:00:47.017501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.