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Paper Citation Record · LEDGER

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems

As of 19 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 4 inbound Pith citation observations for arXiv:2506.08426.

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

pith.paper-citation-record.v1
2506.08426 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:19:18.778384Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:24:39.944675Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T15:26:33.742902Z

Reference resolution

55 of 55 outbound references displayed

  • verified exact0
  • verified fuzzy46
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d0a6cc47-6d9a-46b3-bc3b-a341dbe1ad00 · outbound

This paper cites Optimiz- ing Parameter Mixing Under Constrained Communications in Parallel Federated Learning,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Optimiz- ing Parameter Mixing Under Constrained Communications in Parallel Federated Learning,

Reference 1

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raw_fallback, observed 2026-08-07T05:19:22.135651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 9ecd958f-a29d-44b3-9bea-b4ea6e5120c1 · outbound

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

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models

Reference 2

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no resolver link, observed 2026-08-07T05:19:12.409278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:19:12.409278Z digest=sha256:fbcfe543cc1e68aa753f8c9312d6719e444a9ed00a4ba9985e592a45c4a1f40a

Observation ec085dda-b68c-43c5-8e5e-87667b13b23b · outbound

This paper cites Actions at the Edge: Jointly Optimizing the Resources in Multi-access Edge Computing,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Actions at the Edge: Jointly Optimizing the Resources in Multi-access Edge Computing,

Reference 3

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raw_fallback, observed 2026-08-07T05:19:22.124881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:12.501526Z digest=sha256:363c214aa14df52c266fbd8a45ab0cb5ce263af47b5effc300f938b7832b2ef1

Observation 612aa001-0812-4ccd-b81b-54ad68405feb · outbound

This paper cites Federated Learning over Multihop Wireless Networks with In-network Aggregation,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Federated Learning over Multihop Wireless Networks with In-network Aggregation,

Reference 4

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raw_fallback, observed 2026-08-07T05:19:22.112311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:12.607776Z digest=sha256:9d0dc3a829464636ba71a3ee9f732c10e94a7dee8e5a79c4a4e167a32ceaf4a5

Observation e7523429-d371-443e-8e1a-8b262e5b5729 · outbound

This paper cites Communication-efficient Learning of Deep Networks From Decentral- ized Data,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Communication-efficient Learning of Deep Networks From Decentral- ized Data,

Reference 5

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raw_fallback, observed 2026-08-07T05:19:22.101220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:12.705163Z digest=sha256:9486da5fa68e6019ae64eed426184270efdef96961cd4f24ddb579835a29e1d8

Observation ac121246-1312-45e5-94ca-5fc186f1be8e · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Federated Learning: Strategies for Improving Communication Efficiency

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:19:12.809295Z digest=sha256:5048e7b3aa826e752e610dd99a450be737d0dfb0e066d7e199f4c138c77d0dfd

Observation 202eab37-91c3-4267-8a56-2e649c2fd544 · outbound

This paper cites FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks,

Reference 7

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raw_fallback, observed 2026-08-07T05:19:22.089137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:12.901623Z digest=sha256:fff6bc23dc002d5e9b7781e97f257c6723dfb544bcd329d35f5d22b6fabe6101

Observation 812f4e70-0520-4b88-b767-435c1ad4fa05 · outbound

This paper cites Accelerating Federated Learning with Model Segmentation for Edge Networks,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Accelerating Federated Learning with Model Segmentation for Edge Networks,

Reference 8

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raw_fallback, observed 2026-08-07T05:19:22.036747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:13.004004Z digest=sha256:ee7fc7479892275e4d8cab845a9e2af904ce85301558928acdb0cf562616fb4a

Observation 006ef0b5-57c1-47a2-8eec-c8faca2d4ff2 · outbound

This paper cites Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 9

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no resolver link, observed 2026-08-07T05:19:13.077585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:19:13.077585Z digest=sha256:efbab3e4a6c228899cc23b11c12ca2efba5996c24158e104d41c8f33b0b4bf22

Observation 0fdb6c9f-5907-43a2-89ef-fa34983b424f · outbound

This paper cites LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:19:13.180455Z digest=sha256:699b4ce45618d53ca8d777a122e08bd4e6b5657ab0f3bf96c75060bb67c6e459

Observation 221d8130-e133-4b28-968d-00bb404505f7 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Gemini: A Family of Highly Capable Multimodal Models

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:19:13.277342Z digest=sha256:2ace0a007b2de69423a9b4bb0e16f1363345fe9326c18a585c1784d12001c57d

Observation e3a65ad8-b5de-4ace-a499-7afe87a63600 · outbound

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

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Split learning for health: Distributed deep learning without sharing raw patient data

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:19:13.380159Z digest=sha256:b4737ab94cac611e594ceda1a0d5ccf38335abe60a136929cbf42dd59b578fb1

Observation 6f3bcc76-e2f3-4143-818c-4973a835f7f2 · outbound

This paper cites Pipelining Split Learning in Multi-hop Edge Networks,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Pipelining Split Learning in Multi-hop Edge Networks,

Reference 13

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unresolved
no resolver link, observed 2026-08-07T05:19:13.473112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:19:13.473112Z digest=sha256:4b0aa0d8651acdc4d22d71fa851cea57c13f025bc5c3eb39d5e7ea3d879c7713

Observation d1eb26c1-063a-414f-aefe-030de3327735 · outbound

This paper cites Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.972220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:13.536658Z digest=sha256:21160cf6e5b4447880b6b4e9794b95817a6a5483211bfd0d132e291ef8511a57

Observation a29e315f-6891-4f63-a888-0d4c65a58bd4 · outbound

This paper cites Optimal Resource Allocation for U-Shaped Parallel Split Learning,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Optimal Resource Allocation for U-Shaped Parallel Split Learning,

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.918270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:13.630869Z digest=sha256:c7b597636fff2ea6805351ed39ac77a14528d74cbacb29feeb62d8d0cc694837

Observation f2df321a-2ec8-42d8-804e-f26e632cf10b · outbound

This paper cites Splitfed: When Federated Learning Meets Split Learning,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Splitfed: When Federated Learning Meets Split Learning,

Reference 16

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raw_fallback, observed 2026-08-07T05:19:21.886040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:13.731789Z digest=sha256:500e3dc2e15d5c4f165f63cc93d4f3ec28885e3ab9322bc51027a118fbb8d1d6

Observation a0c05dc7-87b5-4dcc-857f-5fba8090eafa · outbound

This paper cites Distributed Learning in Wireless Networks: Recent Progress and Future Challenges,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Distributed Learning in Wireless Networks: Recent Progress and Future Challenges,

Reference 17

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raw_fallback, observed 2026-08-07T05:19:21.868254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:13.808585Z digest=sha256:4078816191053dab39533c67556f4d2f9037064ae0e9d5da50bff5f18b425fca

Observation eb1da1af-d456-4e92-965d-d89e4f48e6e0 · outbound

This paper cites Time-sensitive Learning For Heterogeneous Federated Edge Intelligence,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Time-sensitive Learning For Heterogeneous Federated Edge Intelligence,

Reference 18

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raw_fallback, observed 2026-08-07T05:19:21.851460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:13.905252Z digest=sha256:94c89524e5420e568ad8ae6d047922b381bfcf44bc007d951ccc89d8765d1b39

Observation aa7e9649-4fcf-4dae-87fa-1c33b6df4bdb · outbound

This paper cites Speeding Up Distributed Machine Learning Using Codes,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Speeding Up Distributed Machine Learning Using Codes,

Reference 19

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raw_fallback, observed 2026-08-07T05:19:21.832698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:14.001903Z digest=sha256:50be3a0eab9301979f6d96434a7ef5f430df607a91d30749c6fad4ea4d368a39

Observation 375ef21c-40f1-440b-adda-c535f143a961 · outbound

This paper cites Split learning over Wireless Networks: Parallel Design and Resource Management,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Split learning over Wireless Networks: Parallel Design and Resource Management,

Reference 20

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raw_fallback, observed 2026-08-07T05:19:21.815858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:14.077784Z digest=sha256:f1f7909dcff27d47afbde601aa7b2ee7f1f473bb630a86787cca344c50043aeb

Observation 9b81a4f8-2a76-4ed7-bcf5-2bd23b75cd99 · outbound

This paper cites AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:19:14.275914Z digest=sha256:f16936437c64fd6260974a4c7ad5b2cc39a17fad8103244927b53f6a96619dd9

Observation 6cfb14a5-0a39-4252-a417-8e2778c70153 · outbound

This paper cites How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?

Reference 22

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:19:14.788824Z digest=sha256:4bd692231ccd4c2db9ab83b28e384b4730de18f0b37da4a256b89580590aa5f1

Observation 9f661f07-6153-4783-bb48-8acac9e49563 · outbound

This paper cites Hi- erarchical Split Federated Learning: Convergence Analysis and System Optimization,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Hi- erarchical Split Federated Learning: Convergence Analysis and System Optimization,

Reference 23

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raw_fallback, observed 2026-08-07T05:19:21.796292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:15.439863Z digest=sha256:5ef0fffaeff549a42963da0489b82c1cf4c6a8d0796efce5510ac50bf30076e1

Observation 841ce698-9d8a-41a4-b7ae-1a09adb073e2 · outbound

This paper cites Adaptive Federated Learning in Resource Constrained Edge Computing Systems,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Adaptive Federated Learning in Resource Constrained Edge Computing Systems,

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.775844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:15.681445Z digest=sha256:d3a8ab563f8b8e16b9959842ba681a824c852ab7d086855ab0a86469cf675183

Observation 6c1e59c9-1d6a-4061-8f79-4ebabaf997f9 · outbound

This paper cites Adaptive Batchsize Selection and Gradient Compression For Wireless Federated Learning,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Adaptive Batchsize Selection and Gradient Compression For Wireless Federated Learning,

Reference 25

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raw_fallback, observed 2026-08-07T05:19:21.756187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:15.881405Z digest=sha256:3cea7a473152a74904b415742b17762c5d3db10108cfdcce4b6cdbbf37574b3c

Observation 57a52eb5-b881-4046-a929-803ac82b1b7a · outbound

This paper cites Adaptive Batch Size For Federated Learning in Resource-constrained Edge Computing,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Adaptive Batch Size For Federated Learning in Resource-constrained Edge Computing,

Reference 26

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raw_fallback, observed 2026-08-07T05:19:21.739916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:15.953841Z digest=sha256:3c9b7e76a335f013b31eb19a9525be2b1696025fb438a836704545a2a01503e7

Observation 567b8293-6966-4165-8337-0d52d5b420a2 · outbound

This paper cites DYNAMITE: Dynamic Interplay of Mini-batch Size and Aggregation Frequency For Federated Learning with Static and Streaming Datasets,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems DYNAMITE: Dynamic Interplay of Mini-batch Size and Aggregation Frequency For Federated Learning with Static and Streaming Datasets,

Reference 27

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raw_fallback, observed 2026-08-07T05:19:21.722528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.056736Z digest=sha256:afc888b6ac1ead298d23f8556ec332c83e1468d931a76feb2685961a051bb1af

Observation 8052b08b-734b-4bf5-93ef-672aa3135444 · outbound

This paper cites To Talk or to Work: Dynamic Batch Sizes Assisted Time Efficient Federated Learning over Future Mobile Edge Devices,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems To Talk or to Work: Dynamic Batch Sizes Assisted Time Efficient Federated Learning over Future Mobile Edge Devices,

Reference 28

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raw_fallback, observed 2026-08-07T05:19:21.703567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.147255Z digest=sha256:2412907247e38a76197993e7f7265ec9f513684a6ceb637c07b6578d68aab7e7

Observation 51335f1b-492a-47bd-9b0a-e71128982769 · outbound

This paper cites Don’t Decay the Learning Rate, Increase the Batch Size,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Don’t Decay the Learning Rate, Increase the Batch Size,

Reference 29

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raw_fallback, observed 2026-08-07T05:19:21.684740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.247764Z digest=sha256:aef5907a2c5ed412e5ddbdb2b65128a4033a9d405082f86aaac6c1600c690a42

Observation 1d418a7c-5544-4f92-9286-7a6d85f1a808 · outbound

This paper cites On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-convex Optimization,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-convex Optimization,

Reference 30

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raw_fallback, observed 2026-08-07T05:19:21.666811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.323361Z digest=sha256:80d672fe3b9ef3c4b41c5abc9811174f79cdecaa46813d36949cf0266ca644d8

Observation 34184eca-cee7-4423-8f46-25c5c03e8ea2 · outbound

This paper cites Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks,

Reference 31

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raw_fallback, observed 2026-08-07T05:19:21.646009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.416994Z digest=sha256:eb5159c0154192a4c4ea10c030ac7c6165e7ade3689eb8b93fcbdb6c4c53f4a7

Observation 0d72df72-4a0b-4172-9f6d-72816efaf638 · outbound

This paper cites Convergence Analysis of Split Federated Learning on Heterogeneous Data,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Convergence Analysis of Split Federated Learning on Heterogeneous Data,

Reference 32

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raw_fallback, observed 2026-08-07T05:19:21.628519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.540756Z digest=sha256:9dfaa31bf3c591704e82a9061e3c06180e90ab9e5ecdc1c420065173d1f7166e

Observation 4ddc86ad-e470-4eef-a5be-e5cb639b9975 · outbound

This paper cites Unleashing the Tiger: Inference Attacks on Split Learning,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Unleashing the Tiger: Inference Attacks on Split Learning,

Reference 33

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raw_fallback, observed 2026-08-07T05:19:21.611964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.613804Z digest=sha256:464787ca67c9821cc9ab2cbe01002629b427bc0186903840f67bbc520b519f00

Observation 4d42c08c-8352-45da-a822-ed5f86db6aa7 · outbound

This paper cites On the Convergence Properties of A K-step Averaging Stochastic Gradient Descent Algorithm for Nonconvex Optimization,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems On the Convergence Properties of A K-step Averaging Stochastic Gradient Descent Algorithm for Nonconvex Optimization,

Reference 34

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raw_fallback, observed 2026-08-07T05:19:21.594511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.691875Z digest=sha256:c2ebe50fcc530642359172f628e94ff11acdbb277387b70f1398b7e2c1928c86

Observation 7165684b-e905-4b9f-9592-3adc5536555a · outbound

This paper cites On the Linear Speedup Analysis of Communication Efficient Momentum SGD For Distributed Non-convex Optimization,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems On the Linear Speedup Analysis of Communication Efficient Momentum SGD For Distributed Non-convex Optimization,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.515224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.813796Z digest=sha256:7d22d9490850401b2211103bc2dd29476290681902165bf9f54441290a7ae124

Observation 5f587a60-b491-448a-a392-4c7aeac51582 · outbound

This paper cites Scaffold: Stochastic Controlled Averaging for Federated Learn- ing,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Scaffold: Stochastic Controlled Averaging for Federated Learn- ing,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.370751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.877069Z digest=sha256:dd887b5eddaf6c5e815d834f16c700a2d3bf32cdd816fd9a0fbb115d7909a258

Observation a9d56ab0-6058-4419-b92f-66efc8d1e430 · outbound

This paper cites Communication-efficient Algorithms for Statistical Optimization,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Communication-efficient Algorithms for Statistical Optimization,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.239009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:16.979085Z digest=sha256:05441370abeeb6b9be8d5326a4438e1006a6e4784f379687f919e22b011a2ef5

Observation fa1e06c5-21ed-4054-a885-cae1b0b37e7f · outbound

This paper cites Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.171479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:17.080067Z digest=sha256:a22b241a23108f545dd57905862644734c968822c7cfb55cd157faa78e8fb58d

Observation 5c411f21-03ff-4da5-94b1-0a752c657cb4 · outbound

This paper cites Perturbed Iterate Analysis for Asynchronous Stochastic Optimization,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Perturbed Iterate Analysis for Asynchronous Stochastic Optimization,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.148655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:17.179275Z digest=sha256:fe3fde85333428bcec9a9d5fa70f82769fd11584c483eaf800570102f8b5a62a

Observation 588b5520-5bb0-4af6-a308-7c27ad4b4557 · outbound

This paper cites Don’t Use Large Mini- batches, Use Local SGD,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Don’t Use Large Mini- batches, Use Local SGD,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.128365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:17.278774Z digest=sha256:d62577442b6129b8221f1169b23008589c0544921f790e73446a70906111ebf3

Observation 825e23b8-9c83-4452-9509-a07d4a103aca · outbound

This paper cites QSFL: Two-Level Communication-Efficient Federated Learning on Mobile Edge Devices,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems QSFL: Two-Level Communication-Efficient Federated Learning on Mobile Edge Devices,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.108706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:17.387424Z digest=sha256:d01f8bd010c5d740c966d2f3fb11541b363701d0219ab528cfae928f5328824b

Observation 1c988741-631a-4b38-87ee-8ddcd667942e · outbound

This paper cites Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.087306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:17.496357Z digest=sha256:8560e11591c469cc656fefa9d44834f2db6f1dc990507c154dc556e5af2f47bf

Observation 9184b8a4-597c-4599-8267-b85f456163e6 · outbound

This paper cites Federated-learning-based Client Scheduling for Low-latency Wireless Communications,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Federated-learning-based Client Scheduling for Low-latency Wireless Communications,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:21.064738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:17.590411Z digest=sha256:20965485bb39e2d43e0cda2860dd8e457f49f6e144a40c5a7514c9fdd25906d6

Observation 63d92231-30dd-4112-882f-782f1292fde7 · outbound

This paper cites Horus: Interference-aware and Prediction-based Scheduling in Deep Learning Systems,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Horus: Interference-aware and Prediction-based Scheduling in Deep Learning Systems,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:20.971770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:17.704855Z digest=sha256:97391f1cb31a82f4d01e46a3dcd7aba0f6717a3443dcba642c277a24bbe1b645

Observation 076c19a0-2d98-4737-aa59-8c5457036e78 · outbound

This paper cites Pipeline Network Simulation Calculation based on Improved Newton Jacobian Iterative Method,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Pipeline Network Simulation Calculation based on Improved Newton Jacobian Iterative Method,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:20.778573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:17.798433Z digest=sha256:7a9a57c92acfeff0a5638dfb02d91d4dad763ad7d57f71aa03eb01bf16fed5d3

Observation 7e618437-7037-4f17-bd63-104329a02ba0 · outbound

This paper cites On Nonlinear Fractional Programming,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems On Nonlinear Fractional Programming,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:20.615924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:17.869444Z digest=sha256:cfea8363bdbcbc0180c2cf62356e8d1ef725b0b97a6ec4d63a202bbae80e5514

Observation fa6987be-2ca3-40be-8e79-c3058720a8d0 · outbound

This paper cites A Reformulation-linearization Method for the Global Optimization of Large-scale Mixed-Integer Linear Fractional Programming Problems and Cyclic Scheduling aApplication,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems A Reformulation-linearization Method for the Global Optimization of Large-scale Mixed-Integer Linear Fractional Programming Problems and Cyclic Scheduling aApplication,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:20.533766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:17.980873Z digest=sha256:1e909f5be1a20ead4bf25d39e1aaa2968ba8b08dd7a3aa856f2d4a1102916bcb

Observation 018b6a45-9d0a-40dc-8d7c-792363b98109 · outbound

This paper cites Extensions of Dinkelbach’s Algorithm for Solving Non-linear Fractional Programming Problems,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Extensions of Dinkelbach’s Algorithm for Solving Non-linear Fractional Programming Problems,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:20.522256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:18.073933Z digest=sha256:0e44ad35449d62ce7a6e32eb6fe6013a65fa0e34d9c027d0b021f6c4caadea4b

Observation 0fb3fcda-f97e-4fdd-9565-ce6df27b30e3 · outbound

This paper cites Convergence of A Block Coordinate Descent Method for Nondifferentiable Minimization,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Convergence of A Block Coordinate Descent Method for Nondifferentiable Minimization,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:20.470069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:18.153863Z digest=sha256:98ddd91c654f2b6a70934ac1a2b8c0f3ee7b44080297da1bb9a04cf93c774d7a

Observation 345f2973-322e-49e9-8306-566eac609b35 · outbound

This paper cites Learning Multiple Layers of Features From Tiny Images,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Learning Multiple Layers of Features From Tiny Images,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:20.271062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:18.242922Z digest=sha256:ca84fc7c1f9218acbf35a1eba29892d887374de5f9a8ab2c26241b79d69b4a7d

Observation d933272e-73e0-41a5-8ad2-2608802ccc68 · outbound

This paper cites Broadband analog aggregation for low-latency federated edge learning,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Broadband analog aggregation for low-latency federated edge learning,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:20.055919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:18.350410Z digest=sha256:cef983b24589f3ac874b93b4965fe0e210d98f0d0cbfdda958c90db7ebaa00b4

Observation f750c997-777d-4e79-b488-11c851a75ac7 · outbound

This paper cites Energy Efficient Federated Learning over Wireless Communication Networks,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Energy Efficient Federated Learning over Wireless Communication Networks,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:19.839516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:18.432885Z digest=sha256:f278397647ad9f7bb1571b4f106e07bc62b4f63f1ed3fd1b4fd4e79dac18a4b7

Observation cb0dfa43-3bfa-4def-a182-54d31f89add2 · outbound

This paper cites Very Deep Convolutional Networks for Large-scale Image Recognition,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Very Deep Convolutional Networks for Large-scale Image Recognition,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:19.630693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:18.565987Z digest=sha256:119eb7d6b5a522c9000ff19aaf76d6dd6b4213b33613d42868678f810b264bb4

Observation 67245bb7-d599-4c7e-a4c2-556ea1c88826 · outbound

This paper cites Deep Residual Learning for Image Recognition,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Deep Residual Learning for Image Recognition,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:19.431604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:18.672200Z digest=sha256:727e0a56b3bebe832f3bedcbcd834097a26e10b6e882c01f5c9d24b9dd999890

Observation a6c6ab4e-da2b-4159-98e3-38d0ec628d5c · outbound

This paper cites CoopFL: Accelerating Federated Learning with DNN Partitioning and Offloading in Hetero- geneous Edge Computing,.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems CoopFL: Accelerating Federated Learning with DNN Partitioning and Offloading in Hetero- geneous Edge Computing,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:19.215331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T05:19:18.778384Z digest=sha256:b92f7cce4ebcb03619786e9dd5ca07e60daa01834f36c9560aef19c4ee4def42

Pith citing papers

Observation 770054e8-dd72-41c5-9768-d654af0509f2 · inbound

A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation cites this paper.

A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-06T19:24:39.944675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:24:39.944675Z digest=sha256:e56d6d4df0b85e4b6b0bbff4a8b3f43dbe86ce71c87ff12cfb10fa2a5411a260

Observation fcba5111-ef59-45a3-a02a-956e155fd767 · inbound

PHandover: Parallel Handover in Mobile Satellite Network cites this paper.

PHandover: Parallel Handover in Mobile Satellite Network HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T18:45:38.778439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:45:38.778439Z digest=sha256:6b0ccf0b20ba45830cb67885291c73fd61c4ffdb8df1059f0a266993b1306c84

Observation 8d6f9294-8b20-4688-862d-7000b3d70693 · inbound

RRTO: A High-Performance Transparent Offloading System for Model Inference in Mobile Edge Computing cites this paper.

RRTO: A High-Performance Transparent Offloading System for Model Inference in Mobile Edge Computing HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T12:30:26.234842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:30:26.234842Z digest=sha256:04836f24cb825b7a07863c5f46b456ee5a7cdd61f0c58ba9e0c602c6be192207

Observation c06dbd26-60a5-42e0-ab7e-5ba5fd6845fb · inbound

Optimizing Split Federated Learning with Unstable Client Participation cites this paper.

Optimizing Split Federated Learning with Unstable Client Participation HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-18T15:26:33.745801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-18T15:24:04.079011Z digest=sha256:4baf92527b37410431d51b294269a089cfa14d368ef6182cb5a2286c5f07cdcf