Paper Citation Record · LEDGER

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices

As of 17 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2502.08692.

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

pith.paper-citation-record.v1
2502.08692 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:52:43.811725Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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 exact1
verified fuzzy29
unresolved5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87961eff-455f-4543-80cf-6626fe65f9f9 · outbound

This paper cites Analysis of three iot-based wireless sensors for environmental monitorings,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Analysis of three iot-based wireless sensors for environmental monitorings,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.394336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a3272b10-343c-4a6b-b651-33f7bad9c953 · outbound

This paper cites Recurrent neural networks for time series classification,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Recurrent neural networks for time series classification,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.377909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1a0c1c9a-589b-401a-b3fd-ef01b8ba584a · outbound

This paper cites An integer- only resource-minimized rnn on fpga for low-frequency sensors in edge- ai,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices An integer- only resource-minimized rnn on fpga for low-frequency sensors in edge- ai,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.362564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7124f939-8374-4b28-9849-15849ac3a98e · outbound

This paper cites Real-time speech recognition for iot purpose using a delta recurrent neural network accelerator,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Real-time speech recognition for iot purpose using a delta recurrent neural network accelerator,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.347451Z

Source-reported events for the cited work

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Observation 3ae0d21c-3b5a-442a-ade6-f37765ec4d0a · outbound

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

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Distributed learning of deep neural network over multiple agents,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.331921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa216e74-2563-4df6-8ca1-e58eca5bbdd6 · outbound

This paper cites Distributed training of deep learning models: A taxonomic perspective,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Distributed training of deep learning models: A taxonomic perspective,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.316845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 26767153-3138-4e10-8097-dc17dea8f5db · outbound

This paper cites Bottlenet++: An end-to-end approach for feature compression in device-edge co-inference systems,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Bottlenet++: An end-to-end approach for feature compression in device-edge co-inference systems,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.301784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 016b80e3-8dd3-47f6-b761-13097e6c02c9 · outbound

This paper cites Unleashing the tiger: Inference attacks on split learning,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Unleashing the tiger: Inference attacks on split learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.286713Z

Source-reported events for the cited work

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Observation cc980d74-34e0-460c-a569-fdd776af2920 · outbound

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

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Split learning for health: Distributed deep learning without sharing raw patient data

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T04:52:43.659957Z

Source-reported events for the cited work

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Observation 4dd48cd9-e764-49a0-8f5d-692e0dc955dc · outbound

This paper cites Improving the communication and computation efficiency of split learning for iot applications,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Improving the communication and computation efficiency of split learning for iot applications,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.271883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ac92c647-f6d4-4c28-8bd6-6366ad9925d2 · outbound

This paper cites Deep learning and reconfigurable platforms in the internet of things: Challenges and opportunities in algorithms and hardware,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Deep learning and reconfigurable platforms in the internet of things: Challenges and opportunities in algorithms and hardware,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.256281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 80bf29f3-5153-4f52-87ad-2ad6e369f61c · outbound

This paper cites LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-08T04:52:43.906927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 664a0377-cc46-4e27-bbff-028913eb1d24 · outbound

This paper cites Fedsl: Federated split learning on distributed sequential data in recurrent neural networks,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Fedsl: Federated split learning on distributed sequential data in recurrent neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.241137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5b5881c9-f2d9-45ef-8b5c-98f6938cda53 · outbound

This paper cites Fpga acceleration of lstm based on data for test flight,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Fpga acceleration of lstm based on data for test flight,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.226024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 32f2355e-93ba-4cd1-b569-1a6de7c5b72e · outbound

This paper cites Mapping multiple lstm models on fpgas,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Mapping multiple lstm models on fpgas,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.210954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5fdd2229-51f5-4e2a-b619-459dae257ff4 · outbound

This paper cites A cloud server oriented fpga accelerator for lstm recurrent neural network,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices A cloud server oriented fpga accelerator for lstm recurrent neural network,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.195609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9f472c32-a296-4718-9805-f245775e731a · outbound

This paper cites Towards the extension of fpg-ai toolflow to rnn deployment on fpgas for on-board satellite applications,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Towards the extension of fpg-ai toolflow to rnn deployment on fpgas for on-board satellite applications,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.180075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 00ad8aa6-f134-4939-b46c-5c7c0cd3b7d8 · outbound

This paper cites An fpga-based lstm acceleration engine for deep learning frameworks,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices An fpga-based lstm acceleration engine for deep learning frameworks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.164657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7fce0ebc-4a47-42fd-b1c4-6f6a4bfd76e0 · outbound

This paper cites Acceleration of lstm with structured pruning method on fpga,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Acceleration of lstm with structured pruning method on fpga,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.149259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 71ef1500-9ff0-4235-b72d-d97cd01a6f68 · outbound

This paper cites Split learning on fpgas,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Split learning on fpgas,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.133053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f25bda71-267e-470d-9604-127f907080f9 · outbound

This paper cites Uav-assisted dis- tributed learning for environmental monitoring in rural environments,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Uav-assisted dis- tributed learning for environmental monitoring in rural environments,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.116718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 562a0973-f884-4dc8-826b-d29c36c97ff7 · outbound

This paper cites Comsplit: A communication–aware split learning design for heterogeneous iot platforms,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Comsplit: A communication–aware split learning design for heterogeneous iot platforms,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.100568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d0e63c94-bd7d-4291-afab-f4f3f388d3bb · outbound

This paper cites Adam: A method for stochastic optimiza- tion,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Adam: A method for stochastic optimiza- tion,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.082442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2d118b4c-5729-4657-876c-cab429e244d6 · outbound

This paper cites Neural Network Quantization for Efficient Inference: A Survey.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Neural Network Quantization for Efficient Inference: A Survey

Reference 24

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Source-reported events for the cited work

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Observation 8d0034a7-7ad6-4bce-8e52-a4e55c9fead4 · outbound

This paper cites Radio frequency fingerprinting on the edge,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Radio frequency fingerprinting on the edge,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.064955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a21c29c8-2aef-4ae6-91a5-ea4e61bb100f · outbound

This paper cites Low-latency in situ image analytics with fpga-based quantized convolutional neural network,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Low-latency in situ image analytics with fpga-based quantized convolutional neural network,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.048672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f0fe43d1-bb9e-4338-96ee-2354f6a553ed · outbound

This paper cites An fpga-based hardware/software design using binarized neural networks for agricultural applications: A case study,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices An fpga-based hardware/software design using binarized neural networks for agricultural applications: A case study,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.031719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 90b3b40d-5016-4458-9d30-8e77bf229766 · outbound

This paper cites Compressing deep neural networks on fpgas to binary and ternary precision with hls4ml,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Compressing deep neural networks on fpgas to binary and ternary precision with hls4ml,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:44.015318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 471ddd61-349b-4404-a462-4374ba221a84 · outbound

This paper cites Compressing large-scale transformer-based models: A case study on BERT,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Compressing large-scale transformer-based models: A case study on BERT,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:43.999289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b82a5cb3-b5b2-4e5e-8cf3-c39b36c7e30e · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Distilling the Knowledge in a Neural Network

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-08T04:52:43.784062Z

Source-reported events for the cited work

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Observation fd29e54c-ccbf-4c48-9347-0fdf5e1f0070 · outbound

This paper cites Quantization and deployment of deep neural networks on microcontrollers,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Quantization and deployment of deep neural networks on microcontrollers,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T04:52:43.789393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b09b86d4-7cd1-4589-b5cf-b00c4a56cb5a · outbound

This paper cites An end-to-end workflow to efficiently compress and deploy dnn classifiers on soc/fpga,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices An end-to-end workflow to efficiently compress and deploy dnn classifiers on soc/fpga,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:43.973911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 77fc5f27-0595-45e6-a915-73eb9c9be808 · outbound

This paper cites Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T04:52:43.800595Z

Source-reported events for the cited work

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Observation f4c30053-0b6e-42c6-b8e4-f0896915d2b1 · outbound

This paper cites Fast inference of deep neural networks in fpgas for particle physics,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Fast inference of deep neural networks in fpgas for particle physics,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:43.957877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 85351717-fd4d-48d9-b4c3-3f433830db62 · outbound

This paper cites Design for portability of reconfigurable virtual instrumentation,.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Design for portability of reconfigurable virtual instrumentation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:52:43.941713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.