Paper Citation Record · LEDGER

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game

As of 15 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2412.16079.

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

pith.paper-citation-record.v1
2412.16079 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:52:19.293172Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

37 of 37 outbound references displayed

verified exact0
verified fuzzy31
unresolved6
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 241b2495-afbf-433d-abd7-0433c3d1e891 · outbound

This paper cites Decentralized machine learning governance: Overview, opportunities, and challenges.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Decentralized machine learning governance: Overview, opportunities, and challenges

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.908473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 38c0730a-12d6-4d74-a4d3-ae2a53016fa2 · outbound

This paper cites A game theory competitive intelligence solution stimulated from a stackelberg game: A three players scenario.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game A game theory competitive intelligence solution stimulated from a stackelberg game: A three players scenario

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.893383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d38c18a8-9a07-442f-8c67-66823286ec6c · outbound

This paper cites Practical secure aggregation for privacy-preserving machine learning.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Practical secure aggregation for privacy-preserving machine learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.877380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4b8bf958-6578-49a0-808c-507728881b7c · outbound

This paper cites Non-cooperative game algorithms for computation offloading in mobile edge computing environments.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Non-cooperative game algorithms for computation offloading in mobile edge computing environments

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.861552Z

Source-reported events for the cited work

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Observation 188b0021-1f4b-422b-b72f-a247d951ddc5 · outbound

This paper cites Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.844211Z

Source-reported events for the cited work

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Observation be1f3b78-97b7-4229-a9c1-d4a096177aec · outbound

This paper cites Towards scalable and efficient deep-rl in edge computing: A game-based partition approach.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Towards scalable and efficient deep-rl in edge computing: A game-based partition approach

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.827382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c3e44eb1-cb4d-45c1-9a25-cef0c7561e02 · outbound

This paper cites Improving federated learning with quality-aware user incentive and auto-weighted model aggregation.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Improving federated learning with quality-aware user incentive and auto-weighted model aggregation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.810085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 61eab564-fba4-4125-bea9-e9882f58d966 · outbound

This paper cites Revisiting Fundamentals of Experience Replay.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Revisiting Fundamentals of Experience Replay

Reference 8

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Source-reported events for the cited work

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Observation 55814623-fe97-4b65-a2b7-505e036efda4 · outbound

This paper cites Prevalence, age distribution, and gender of patients with atrial fibrillation: analysis and implications.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Prevalence, age distribution, and gender of patients with atrial fibrillation: analysis and implications

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.794698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 846f49f0-8a4c-4513-81c5-a505e4f7a76c · outbound

This paper cites Game theory.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Game theory

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.779823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 03ad4677-aff4-4a14-ada3-64411d3f9a75 · outbound

This paper cites The non-iid data quagmire of decentralized machine learning.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game The non-iid data quagmire of decentralized machine learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.764896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9a2b6f3a-0005-48e2-b8dd-b324e38c9a19 · outbound

This paper cites Online task scheduling for edge computing based on repeated stackelberg game.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Online task scheduling for edge computing based on repeated stackelberg game

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.749215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 613bea9c-6640-4eb3-bae0-f6ae4ab7179b · outbound

This paper cites Key challenges for delivering clinical impact with artificial intelligence.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Key challenges for delivering clinical impact with artificial intelligence

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.733128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aef424eb-541d-4bf3-a29f-852c6d0fceca · outbound

This paper cites Hierarchical aerial offload computing algorithm based on the stackelberg-evolutionary game model.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Hierarchical aerial offload computing algorithm based on the stackelberg-evolutionary game model

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.718324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4f2d9fbf-b43b-4aa2-a1f2-f3b35a3831e1 · outbound

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

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Federated Learning: Strategies for Improving Communication Efficiency

Reference 15

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Source-reported events for the cited work

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Observation 56b09ec0-62d1-4a48-bd94-0a291618f452 · outbound

This paper cites Federated learning based on stackelberg game in unmanned- aerial-vehicle-enabled mobile edge computing.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Federated learning based on stackelberg game in unmanned- aerial-vehicle-enabled mobile edge computing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.703302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8400a30d-a9d8-498b-9de0-60f2b58c00e6 · outbound

This paper cites On the Convergence of FedAvg on Non-IID Data.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game On the Convergence of FedAvg on Non-IID Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T10:52:19.191480Z

Source-reported events for the cited work

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Observation 900ea3df-084f-478f-a5d7-2d0a81b03bc3 · outbound

This paper cites Robust and scalable federated learning framework for client data heterogeneity based on optimal clustering.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Robust and scalable federated learning framework for client data heterogeneity based on optimal clustering

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.688075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f6d09d32-0d4c-468d-a30c-c2c2456c7971 · outbound

This paper cites On the dirichlet distribution.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game On the dirichlet distribution

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.672629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e860de83-19b3-45f8-8d01-1e6f4a629944 · outbound

This paper cites No fear of heterogeneity: Classifier calibration for federated learning with non-iid data.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game No fear of heterogeneity: Classifier calibration for federated learning with non-iid data

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.656553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bdb1cdf8-8577-4612-8c76-c73bbe8bdba5 · outbound

This paper cites Addressing bias in artificial intelligence in health care.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Addressing bias in artificial intelligence in health care

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.639655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d6e76f8f-e2d0-4f6c-a3df-2abfd5cb9d4f · outbound

This paper cites Rural–urban distribution of the us geriatrics physician workforce.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Rural–urban distribution of the us geriatrics physician workforce

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.623619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 223c24e6-0a81-416b-be6e-bcf3f39ba9af · outbound

This paper cites Precision-Weighted Federated Learning.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Precision-Weighted Federated Learning

Reference 23

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Source-reported events for the cited work

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Observation 622503e6-47bd-4991-bad6-3a4dfdba0434 · outbound

This paper cites Simaan and J.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Simaan and J

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.607748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e14f18c7-37b2-4ac6-9bb4-bc684002b2e4 · outbound

This paper cites Simaan and J.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Simaan and J

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.591046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a7c35bb4-5ef8-4fa8-a94c-06a02ce17c1a · outbound

This paper cites Stackelberg evolutionary game theory: how to manage evolving systems.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Stackelberg evolutionary game theory: how to manage evolving systems

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.573543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 003a4a64-d17c-4444-9d39-b805120040f9 · outbound

This paper cites Knowledge gap regarding osteoporosis among medical professionals in southern india.Journal of Evaluation in Clinical Practice, 26(1):272–280, 2020.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Knowledge gap regarding osteoporosis among medical professionals in southern india.Journal of Evaluation in Clinical Practice, 26(1):272–280, 2020

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.557087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cee4b6aa-a6e1-4a24-8b86-300c816aacae · outbound

This paper cites Theory of games and economic behavior.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Theory of games and economic behavior

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.540617Z

Source-reported events for the cited work

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Observation f4ce48e6-c281-4e11-96a0-471871a880e8 · outbound

This paper cites Optimizing federated learning on non-iid data with reinforcement learning.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Optimizing federated learning on non-iid data with reinforcement learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.524848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 93d176fc-294c-4d07-883f-30adafc40475 · outbound

This paper cites Understanding shortages of sufficient health care in rural areas.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Understanding shortages of sufficient health care in rural areas

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.508253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7e2257ea-ad5d-415c-a1a2-be7af2dbf22e · outbound

This paper cites Fast-convergent federated learning with adaptive weighting.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Fast-convergent federated learning with adaptive weighting

Reference 31

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Source-reported events for the cited work

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Observation 7b951fe3-aa6c-4795-be5b-f3cff20f657a · outbound

This paper cites Node selection toward faster convergence for federated learning on non-iid data.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Node selection toward faster convergence for federated learning on non-iid data

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.480907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 834a64ad-7577-4450-af60-7ec4a1512eaf · outbound

This paper cites Supply–demand balancing for power management in smart grid: A stackelberg game approach.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Supply–demand balancing for power management in smart grid: A stackelberg game approach

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.464152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 69b7aacc-7ae0-4b56-88c9-c1f28b93c09a · outbound

This paper cites Towards value-sensitive and poisoning-proof model aggregation for federated learning on heterogeneous data.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Towards value-sensitive and poisoning-proof model aggregation for federated learning on heterogeneous data

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.448682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ce8e1670-a7fb-4879-a86f-940eede574e0 · outbound

This paper cites Fedsw: Federated learning with adaptive sample weights.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Fedsw: Federated learning with adaptive sample weights

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.432531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 44f07235-9687-43e3-99a2-fc9c47ca14d8 · outbound

This paper cites Federated Learning with Non-IID Data.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Federated Learning with Non-IID Data

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T10:52:19.288232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebd80f89-2752-451e-9051-4a4e328a9ab7 · outbound

This paper cites Modeling demand response in electricity retail markets as a stackelberg game.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Modeling demand response in electricity retail markets as a stackelberg game

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:52:19.416243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.