Paper Citation Record · LEDGER

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning

As of 1 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2504.09114.

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

pith.paper-citation-record.v1
2504.09114 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T21:07:40.706681Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-01T06:32:01.292127+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-05T08:00:46.961263Z

Reference resolution

38 of 38 outbound references displayed

verified exact4
verified fuzzy33
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a35f74e8-5fbe-4b34-a92a-954f75c8f5de · outbound

This paper cites Big ai models for 6g wireless networks: Opportunities, challenges, and research directions.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Big ai models for 6g wireless networks: Opportunities, challenges, and research directions

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.647922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 0f6db1af-c122-4321-9a18-a1d263f62bc2 · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Resource allocation for stable llm training in mobile edge computing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.644225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8999a360-7468-4127-9296-c9a7a79cf72e · outbound

This paper cites Large language models in medicine.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Large language models in medicine

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.636866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation d26983a0-b370-4609-8c1e-17d261380895 · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning BloombergGPT: A Large Language Model for Finance

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-22T21:12:08.750411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 9149c54d-fd97-49cc-a49c-6d30481f36dd · outbound

This paper cites Efficient federated learning for modern nlp.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Efficient federated learning for modern nlp

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.640916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 0a698249-d103-4bc0-ad3e-beef94720b4d · outbound

This paper cites Federated learning for predicting clinical outcomes in patients with covid-19.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Federated learning for predicting clinical outcomes in patients with covid-19

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.630178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7a88db02-eb06-4195-8bb4-608165d1a8c2 · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Openfedllm: Training large language models on decentralized private data via federated learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.626333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation cd7e2188-bd43-473b-8bde-338144aa17c7 · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Splitfed: When federated learning meets split learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.623066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation de7f9632-83fe-4209-b82e-26dd51f45894 · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Adaptive and parallel split federated learning in vehicular edge computing

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.617865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation cdcfb6da-75e8-46e9-b116-7ba9db8879f3 · outbound

This paper cites Split feder- ated learning empowered vehicular edge intelligence: Concept, adaptive design, and future directions.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Split feder- ated learning empowered vehicular edge intelligence: Concept, adaptive design, and future directions

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.614225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 70707ac3-3604-461d-aaff-f59f0ca9c75c · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Imagenet: A large-scale hierarchical image database

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.610587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 75ef8be7-fb96-4351-8e65-367b53d21c59 · outbound

This paper cites Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.607578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 87ee5227-06d2-4151-b36c-a983b8881e59 · outbound

This paper cites Attention is all you need.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Attention is all you need

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.604271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 4d716c9f-f4c9-407c-8943-0a3b2269ea84 · outbound

This paper cites Interior point methods for nonlinear optimization.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Interior point methods for nonlinear optimization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.601434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ba1ef4a4-432c-4afb-b69d-bed79a4b239b · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-22T21:12:08.744925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ce137014-86f6-4949-9605-ca178a57a22f · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Parameter-efficient transfer learning for nlp

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.597807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation dfa69679-f7c0-4b17-8983-3df29143ede5 · outbound

This paper cites Prompt distillation for efficient llm-based recommendation.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Prompt distillation for efficient llm-based recommendation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.594333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 90875d2e-bf2f-4e54-b5a5-ab5f44f6daaa · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Lora: Low-rank adaptation of large language models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.590247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation a346613e-6c00-49db-8bc0-f12b0ee1a315 · outbound

This paper cites Game-theoretic power allocation and client selection for privacy-preserving federated learning in IoMT.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Game-theoretic power allocation and client selection for privacy-preserving federated learning in IoMT

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.586684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation cb35270d-e6ad-49bd-8334-89188c5b1ee4 · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Joint accuracy and latency optimization for quantized federated learning in vehicular networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.583487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation c341dd64-af47-4d6c-bd45-1d174b04bbc0 · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning 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 2026-05-22T21:35:13.580223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8b7e7779-177d-4e9f-a569-dd16e3f9d11c · outbound

This paper cites Fesvibs: Federated split learning of vision transformer with block sampling.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Fesvibs: Federated split learning of vision transformer with block sampling

Reference 22

Resolution: verified exact; doi, observed 2026-05-22T21:12:08.262601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 19a617ab-3b43-4c49-9f44-7396aec4cdb1 · outbound

This paper cites Model partition and resource allocation for split learning in vehicular edge networks.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Model partition and resource allocation for split learning in vehicular edge networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.576957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation cb81838a-dd18-4fa0-a247-fbe7d955ebd5 · outbound

This paper cites Sparse-tuning: Adapting vision transformers with efficient fine-tuning and inference.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Sparse-tuning: Adapting vision transformers with efficient fine-tuning and inference

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:12:08.756034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 0005089f-4940-4860-a216-aa7dbe9e2355 · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Quantized federated learning under transmission delay and outage constraints

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.573086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ab9e9db5-d153-4603-8bb2-d6c164bf43ae · outbound

This paper cites Training quantized nets: A deeper understanding.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Training quantized nets: A deeper understanding

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.570179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 3e066d08-4bb5-4bd9-ba49-e12b2cd714b1 · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Service delay minimization for federated learning over mobile devices

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.567010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation d0a909de-105d-4ae3-9243-4a78c9cb5736 · outbound

This paper cites Green, quantized federated learning over wireless networks: An energy-efficient design.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Green, quantized federated learning over wireless networks: An energy-efficient design

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.563729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 29005efe-cdf8-4809-a1b2-ac757e0477a7 · outbound

This paper cites Convergence analysis of split federated learning on heterogeneous data.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Convergence analysis of split federated learning on heterogeneous data

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.560965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 831a7293-3b4d-4ea4-9c5a-47f4c5436c7a · outbound

This paper cites On the convergence of fedavg on non-iid data.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning On the convergence of fedavg on non-iid data

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.557827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 2ef7500c-7b31-4b37-962f-055c8db65c6e · outbound

This paper cites Federated learning on the road autonomous controller design for connected and autonomous vehicles.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Federated learning on the road autonomous controller design for connected and autonomous vehicles

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.554577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 2feac308-6549-4e8a-bedb-8fb66ecbc04a · outbound

This paper cites A unified theory of decentralized sgd with changing topology and local updates.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning A unified theory of decentralized sgd with changing topology and local updates

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.551441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 909e4497-a135-43f6-b70a-2500891e80a0 · outbound

This paper cites A unified analysis of federated learning with arbitrary client participation.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning A unified analysis of federated learning with arbitrary client participation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.548258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation acd7da51-29b8-48f7-a045-300c758538dd · outbound

This paper cites Robust federated learning for unreliable and resource-limited wireless networks.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Robust federated learning for unreliable and resource-limited wireless networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.544564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 2cedf262-3e2d-4aa6-b72c-7cc3d1f02cc8 · outbound

This paper cites Schrijver et al., Combinatorial optimization: polyhedra and efficiency.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Schrijver et al., Combinatorial optimization: polyhedra and efficiency

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.538827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 2c41ea11-7b02-423e-a257-23e91d30d876 · outbound

This paper cites A new polynomial-time algorithm for linear program- ming.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning A new polynomial-time algorithm for linear program- ming

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.535485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b42635d0-4bec-430f-9002-27ca8289e2db · outbound

This paper cites an unresolved cited work.

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-05-22T21:35:13.532470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b64091ce-b04e-46ee-b4f4-8217b386275e · outbound

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

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning Learning multiple layers of features from tiny images

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.529379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Pith citing papers

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

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

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 2026-08-01T06:32:01.292127+00:00.