Paper Citation Record · LEDGER

Optimizing Split Federated Learning with Unstable Client Participation

As of 19 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2509.17398.

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

pith.paper-citation-record.v1
2509.17398 v2

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T15:24:04.079011Z

measured 62 of 62 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T05:02:18.746700Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-11T21:36:13.686374Z

Reference resolution

61 of 61 outbound references displayed

verified exact12
verified fuzzy49
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3ec27c48-4c78-4e64-86ac-75d262b216f4 · outbound

This paper cites 2023 edge AI technology report.

Optimizing Split Federated Learning with Unstable Client Participation 2023 edge AI technology report

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.441330Z

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.

Observation f603b4f7-3251-44cc-affb-7f487a07acaf · outbound

This paper cites MADRL-based model partition- ing, aggregation control, and resource allocation for cloud-edge-device collaborative split federated learning.

Optimizing Split Federated Learning with Unstable Client Participation MADRL-based model partition- ing, aggregation control, and resource allocation for cloud-edge-device collaborative split federated learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.445086Z

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.

Observation f71314d3-68b4-4d71-93cc-0511a9946db5 · outbound

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

Optimizing Split Federated Learning with Unstable Client Participation Split learning over wireless networks: Parallel design and resource management

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.408576Z

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.

Observation 3ab017ee-d5eb-4ea7-b39c-3c286065a6bb · outbound

This paper cites Pipelining split learning in multi-hop edge networks.

Optimizing Split Federated Learning with Unstable Client Participation Pipelining split learning in multi-hop edge networks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:26:33.759984Z

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.

Observation 237502f1-0631-486c-a5d2-d8d3c44ee280 · outbound

This paper cites Spectrum breathing: Protecting over-the-air federated learning against interference.

Optimizing Split Federated Learning with Unstable Client Participation Spectrum breathing: Protecting over-the-air federated learning against interference

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.380029Z

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.

Observation 3c2a5a05-427e-4884-b9cc-fed9a0a3ba20 · outbound

This paper cites 3U: Joint design of UA V-USV-UUV networks for cooperative target hunting.

Optimizing Split Federated Learning with Unstable Client Participation 3U: Joint design of UA V-USV-UUV networks for cooperative target hunting

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.459514Z

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.

Observation a2573c2e-76ce-40a4-b2e9-603192c0b7f9 · outbound

This paper cites Underwater differential game: Finite-time target hunting task with communication delay.

Optimizing Split Federated Learning with Unstable Client Participation Underwater differential game: Finite-time target hunting task with communication delay

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.466217Z

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.

Observation 99560d55-ca82-4d69-a50c-c96b7138f691 · outbound

This paper cites Differential game-based deep reinforcement learning in underwater target hunting task.

Optimizing Split Federated Learning with Unstable Client Participation Differential game-based deep reinforcement learning in underwater target hunting task

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.373409Z

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.

Observation d6a11164-5a22-4bc3-8b2e-0b2a3a74a03a · outbound

This paper cites NVIDIA Jetson Xavier.

Optimizing Split Federated Learning with Unstable Client Participation NVIDIA Jetson Xavier

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.424028Z

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.

Observation 542c3f82-7dc5-49a1-be8a-26ce153ed520 · outbound

This paper cites Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data.

Optimizing Split Federated Learning with Unstable Client Participation Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.449035Z

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.

Observation ce4b2f3c-8f12-49a2-ae91-5b4963136939 · outbound

This paper cites Split Learning in 6G Edge Networks.

Optimizing Split Federated Learning with Unstable Client Participation Split Learning in 6G Edge Networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.386638Z

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.

Observation 8a48b149-99b0-44dc-9841-f81712320753 · outbound

This paper cites Pairingfl: Efficient federated learning with model splitting and client pairing.

Optimizing Split Federated Learning with Unstable Client Participation Pairingfl: Efficient federated learning with model splitting and client pairing

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.405258Z

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.

Observation 6937e0a8-2972-4931-8166-93cfb4a30e4a · outbound

This paper cites Efficient parallel split learning over resource-constrained wireless edge networks.

Optimizing Split Federated Learning with Unstable Client Participation Efficient parallel split learning over resource-constrained wireless edge networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.402188Z

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.

Observation 258a4c42-3c68-4b13-b94e-99e76ab6bea1 · outbound

This paper cites Leo-split: A semi-supervised split learning framework over leo satellite networks.

Optimizing Split Federated Learning with Unstable Client Participation Leo-split: A semi-supervised split learning framework over leo satellite networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.370250Z

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.

Observation da46627a-5382-40d8-bdc2-1ad6f4809439 · outbound

This paper cites Federated learning: Challenges, methods, and future directions.

Optimizing Split Federated Learning with Unstable Client Participation Federated learning: Challenges, methods, and future directions

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.360043Z

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.

Observation 6aec83a4-c823-4a9b-9c45-cf6cfa66a368 · outbound

This paper cites Fedsn: A federated learning framework over heterogeneous leo satellite networks.

Optimizing Split Federated Learning with Unstable Client Participation Fedsn: A federated learning framework over heterogeneous leo satellite networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.416181Z

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.

Observation 32b5bf43-40f9-4436-b1af-8780d23371f0 · outbound

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

Optimizing Split Federated Learning with Unstable Client Participation Federated Learning: Strategies for Improving Communication Efficiency

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-18T15:26:33.792288Z

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.

Observation 5a0f7072-0538-467d-813d-0f6c1e2ba2d1 · outbound

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

Optimizing Split Federated Learning with Unstable Client Participation Splitfed: When federated learning meets split learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.392738Z

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.

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

This paper cites HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems.

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.

Observation be6399a9-e092-42d8-b6fa-628924710a32 · outbound

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

Optimizing Split Federated Learning with Unstable Client Participation Wireless distributed learning: A new hybrid split and federated learning approach

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.438239Z

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.

Observation 044d148c-8c73-4f1f-9f57-0d7be1200d1e · outbound

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

Optimizing Split Federated Learning with Unstable Client Participation HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:26:33.751108Z

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.

Observation 505cb576-abf2-4ad5-bed4-b2a03ed1346b · outbound

This paper cites Federated learning in mobile edge networks: A comprehensive survey.

Optimizing Split Federated Learning with Unstable Client Participation Federated learning in mobile edge networks: A comprehensive survey

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.389757Z

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.

Observation 90b0081b-6506-44b6-ab4d-a1ad26015126 · outbound

This paper cites Fed- erated learning under heterogeneous and correlated client availability.

Optimizing Split Federated Learning with Unstable Client Participation Fed- erated learning under heterogeneous and correlated client availability

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.501889Z

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.

Observation e502b2ea-8c45-40c3-b4e7-cf86172feb25 · outbound

This paper cites FedMeld: A model-dispersal feder- ated learning framework for space-ground integrated networks.

Optimizing Split Federated Learning with Unstable Client Participation FedMeld: A model-dispersal feder- ated learning framework for space-ground integrated networks

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:26:33.805001Z

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.

Observation e888af87-d67e-4fa6-85f6-02b67072bde5 · outbound

This paper cites Accelerating federated learning with model segmentation for edge networks.

Optimizing Split Federated Learning with Unstable Client Participation Accelerating federated learning with model segmentation for edge networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.395849Z

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.

Observation d2f7f724-b21d-4d7a-8d71-1cb2d988f598 · outbound

This paper cites Smart split-federated learning over noisy channels for embryo image segmentation.

Optimizing Split Federated Learning with Unstable Client Participation Smart split-federated learning over noisy channels for embryo image segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.356345Z

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.

Observation e642ab64-1da0-4999-98de-db997ff55aef · outbound

This paper cites SplitFed resilience to packet loss: Where to split, that is the question.

Optimizing Split Federated Learning with Unstable Client Participation SplitFed resilience to packet loss: Where to split, that is the question

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.452554Z

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.

Observation 9d8dfd48-820d-4408-a3bd-4524e498ab17 · outbound

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

Optimizing Split Federated Learning with Unstable Client Participation Communication-efficient learning of deep networks from decentralized data

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.398867Z

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.

Observation ff83b55d-db8a-466f-a4fb-dafa27a9ad82 · outbound

This paper cites Cooperative SGD: a unified framework for the design and analysis of local-update sgd algorithms.

Optimizing Split Federated Learning with Unstable Client Participation Cooperative SGD: a unified framework for the design and analysis of local-update sgd algorithms

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.469329Z

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.

Observation e098f5fa-ef47-499a-9870-5084705f5049 · outbound

This paper cites Graph oracle models, lower bounds, and gaps for parallel stochastic optimization.

Optimizing Split Federated Learning with Unstable Client Participation Graph oracle models, lower bounds, and gaps for parallel stochastic optimization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.491849Z

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.

Observation 01ad84ba-4989-4ba4-b294-78a086f70120 · outbound

This paper cites Federated learning over wireless networks: Convergence analysis and resource allocation.

Optimizing Split Federated Learning with Unstable Client Participation Federated learning over wireless networks: Convergence analysis and resource allocation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.383577Z

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.

Observation 1bc55b9b-dbbb-477f-9aec-e41afbded25d · outbound

This paper cites Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies.

Optimizing Split Federated Learning with Unstable Client Participation Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:26:33.771784Z

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.

Observation 7e87d133-6fd2-4b16-a5e9-5e68d63f03e7 · outbound

This paper cites Optimal Client Sampling for Federated Learning.

Optimizing Split Federated Learning with Unstable Client Participation Optimal Client Sampling for Federated Learning

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:26:33.786095Z

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.

Observation df4dd5dc-2f90-4428-9888-64cc1cf2056b · outbound

This paper cites Federated learning under impor- tance sampling.

Optimizing Split Federated Learning with Unstable Client Participation Federated learning under impor- tance sampling

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.482172Z

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.

Observation 5862dbe3-f364-45d2-931f-a36332aaaa78 · outbound

This paper cites Towards understanding biased client selection in federated learning.

Optimizing Split Federated Learning with Unstable Client Participation Towards understanding biased client selection in federated learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.363331Z

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.

Observation 81ae1d54-99b0-401b-a490-ee17b16b931b · outbound

This paper cites Clustered sampling: Low-variance and improved representativity for clients selection in federated learning.

Optimizing Split Federated Learning with Unstable Client Participation Clustered sampling: Low-variance and improved representativity for clients selection in federated learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.462555Z

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.

Observation 8e73e250-e092-4007-b8ad-d6aff0220e97 · outbound

This paper cites Heterogeneity-guided client sampling: Towards fast and efficient Non-IID federated learning.

Optimizing Split Federated Learning with Unstable Client Participation Heterogeneity-guided client sampling: Towards fast and efficient Non-IID federated learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.485402Z

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.

Observation a22f1134-cedc-43f8-862b-e8bf87a01c75 · outbound

This paper cites Tackling system and statistical heterogeneity for federated learning with adaptive client sampling.

Optimizing Split Federated Learning with Unstable Client Participation Tackling system and statistical heterogeneity for federated learning with adaptive client sampling

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.430674Z

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.

Observation 6714c528-6bc9-4f5e-aabc-70eb7c88d655 · outbound

This paper cites Eiffel: Efficient and fair scheduling in adaptive federated learning.

Optimizing Split Federated Learning with Unstable Client Participation Eiffel: Efficient and fair scheduling in adaptive federated learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.494826Z

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.

Observation 2e68aa7f-6d70-4e3e-bec5-745aa3575acb · outbound

This paper cites Ultra-low- latency edge inference for distributed sensing.

Optimizing Split Federated Learning with Unstable Client Participation Ultra-low- latency edge inference for distributed sensing

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.427237Z

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.

Observation 5c1ee379-0dd3-42c0-bf35-56c138a8eb05 · outbound

This paper cites Revisiting outage for edge inference systems.

Optimizing Split Federated Learning with Unstable Client Participation Revisiting outage for edge inference systems

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:26:33.765849Z

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.

Observation 8d2a68b6-27d0-46d4-b2dd-8293f3f4df66 · outbound

This paper cites Adaptsfl: Adaptive split federated learning in resource-constrained edge networks.

Optimizing Split Federated Learning with Unstable Client Participation Adaptsfl: Adaptive split federated learning in resource-constrained edge networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.455838Z

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.

Observation f3e4ed7c-99b0-4e28-b208-e0ce2ff114c9 · outbound

This paper cites Hierarchical split federated learning: Convergence analysis and system optimization.

Optimizing Split Federated Learning with Unstable Client Participation Hierarchical split federated learning: Convergence analysis and system optimization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.411970Z

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.

Observation 5507b370-9011-4382-8aff-9fd44d876dbd · outbound

This paper cites Accelerating split federated learning over wireless communication networks.

Optimizing Split Federated Learning with Unstable Client Participation Accelerating split federated learning over wireless communication networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.420581Z

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.

Observation a9f2b48b-8b84-4ff5-91f3-ca2581f59199 · outbound

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

Optimizing Split Federated Learning with Unstable Client Participation Unleashing the tiger: Inference attacks on split learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.472415Z

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.

Observation a65cd979-9e07-4840-81a2-4ec2fc38124a · outbound

This paper cites SCAFFOLD: Stochastic controlled averaging for federated learning.

Optimizing Split Federated Learning with Unstable Client Participation SCAFFOLD: Stochastic controlled averaging for federated learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.475579Z

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.

Observation 5f6896ab-361e-445b-ae23-0e187606b14f · outbound

This paper cites On the conver- gence of fedavg on Non-IID data.

Optimizing Split Federated Learning with Unstable Client Participation On the conver- gence of fedavg on Non-IID data

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.434669Z

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.

Observation cbec7a1a-382a-4027-80fa-0a5de3c1c225 · outbound

This paper cites Achieving linear speedup with partial worker participation in non-iid federated learning.

Optimizing Split Federated Learning with Unstable Client Participation Achieving linear speedup with partial worker participation in non-iid federated learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.478712Z

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.

Observation d8e5c454-e47e-4895-a5c7-4cd45a4004ba · outbound

This paper cites Resource constrained vehicular edge federated learning with highly mobile connected vehicles.

Optimizing Split Federated Learning with Unstable Client Participation Resource constrained vehicular edge federated learning with highly mobile connected vehicles

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.498379Z

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.

Observation 295f34fe-a6f5-4cbf-8a45-dd695a665244 · outbound

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

Optimizing Split Federated Learning with Unstable Client Participation Convergence Analysis of Split Federated Learning on Heterogeneous Data

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:26:33.740762Z

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Observation 4f4f3b8c-909a-4c14-8eef-127136cd4c81 · outbound

This paper cites On the convergence of local stochastic compositional gradient descent with momentum.

Optimizing Split Federated Learning with Unstable Client Participation On the convergence of local stochastic compositional gradient descent with momentum

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.376865Z

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Observation b85938c2-440d-4ebc-a1dd-467dd5a1bb8e · outbound

This paper cites Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity.

Optimizing Split Federated Learning with Unstable Client Participation Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity

Reference 52

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Observation 36dd018f-8414-4b63-a9bb-afc99f92d986 · outbound

This paper cites Adaptive heterogeneous client sampling for federated learning over wireless networks.

Optimizing Split Federated Learning with Unstable Client Participation Adaptive heterogeneous client sampling for federated learning over wireless networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.366525Z

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Observation 5852b79f-b04b-4bfb-bdc8-81635d70ba45 · outbound

This paper cites A bisection method for systems of nonlinear equations.

Optimizing Split Federated Learning with Unstable Client Participation A bisection method for systems of nonlinear equations

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.488590Z

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This paper cites EMNIST: an extension of MNIST to handwritten letters.

Optimizing Split Federated Learning with Unstable Client Participation EMNIST: an extension of MNIST to handwritten letters

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-18T15:26:33.799540Z

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Observation 0226d041-ce0a-4d18-83ae-140918e630e1 · outbound

This paper cites Gradient-based learning applied to document recognition.

Optimizing Split Federated Learning with Unstable Client Participation Gradient-based learning applied to document recognition

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.343261Z

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Observation 5c52b4f9-e4a1-44ae-be25-7b85bf5e8ed1 · outbound

This paper cites Deep residual learning for image recognition.

Optimizing Split Federated Learning with Unstable Client Participation Deep residual learning for image recognition

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.340555Z

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Observation 9b0da58f-9023-4447-af46-f9a9625901ba · outbound

This paper cites Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning.

Optimizing Split Federated Learning with Unstable Client Participation Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:26:33.735807Z

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Observation ab2851a4-f638-4cb4-9aec-15cda198ad4b · outbound

This paper cites Adaptive federated learning in resource constrained edge com- puting systems.

Optimizing Split Federated Learning with Unstable Client Participation Adaptive federated learning in resource constrained edge com- puting systems

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.352828Z

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Observation 6c1bc0d3-aa5d-4988-a773-901180df2c09 · outbound

This paper cites DTS: A simulator to estimate the training time of distributed deep neural networks.

Optimizing Split Federated Learning with Unstable Client Participation DTS: A simulator to estimate the training time of distributed deep neural networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.349492Z

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Observation 64994bff-427f-4a02-9d8a-5565b9195554 · outbound

This paper cites Modeling forecast errors for microgrid operation using Gaussian process regression.

Optimizing Split Federated Learning with Unstable Client Participation Modeling forecast errors for microgrid operation using Gaussian process regression

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T15:26:34.346155Z

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Pith citing papers

Observation dbe98b53-b2cc-4996-a74d-eb43c185b32a · inbound

FluxShard: Motion-Aware Feature Cache Reuse for Collaborative Video Analytics in Mobile Edge Computing cites this paper.

FluxShard: Motion-Aware Feature Cache Reuse for Collaborative Video Analytics in Mobile Edge Computing Optimizing Split Federated Learning with Unstable Client Participation

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-11T21:36:13.692687Z

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