Paper Citation Record · LEDGER

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning

As of 15 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2412.00980.

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

pith.paper-citation-record.v1
2412.00980 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:57:46.592128Z

measured 39 of 39 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

39 of 39 outbound references displayed

verified exact2
verified fuzzy25
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1160cc2f-7550-4285-9820-769c8585602a · outbound

This paper cites Mitigating Bias in Federated Learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Mitigating Bias in Federated Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T04:57:46.406918Z

Source-reported events for the cited work

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Observation d679aad4-d509-4b3c-bfc1-0e0e4d685202 · outbound

This paper cites Byzantine stochastic gradient descent.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Byzantine stochastic gradient descent

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.349041Z

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 de42715f-9f26-41ba-bb7f-9734655ca553 · outbound

This paper cites Machine learning with adversaries: Byzantine tolerant gradient descent.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Machine learning with adversaries: Byzantine tolerant gradient descent

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.332158Z

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 20f10294-6456-48c8-880f-1e7765d88d24 · outbound

This paper cites One for one, or all for all: Equilibria and optimality of collaboration in federated learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning One for one, or all for all: Equilibria and optimality of collaboration in federated learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.315750Z

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 c0faeeaa-b0a7-4821-82be-fb2a213ab7c8 · outbound

This paper cites Optimization methods for large-scale machine learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Optimization methods for large-scale machine learning

Reference 5

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Source-reported events for the cited work

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Observation efb2cd45-7e6c-4f2b-80fc-a2b5c47403dd · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning LEAF: A Benchmark for Federated Settings

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T04:57:46.434109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1f38079-63c7-4939-a0de-6ab53aee80b8 · outbound

This paper cites Linear Speedup in Personalized Collaborative Learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Linear Speedup in Personalized Collaborative Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T04:57:46.439847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d64f6687-b711-4b82-9f43-2627e52d7b88 · outbound

This paper cites On a stochastic approximation method.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning On a stochastic approximation method

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.289368Z

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 acf1e533-6119-49ae-93f2-f8236cdcb49f · outbound

This paper cites Model-sharing games: Analyzing federated learning under voluntary participation.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Model-sharing games: Analyzing federated learning under voluntary participation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.274807Z

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 620f3882-fc0c-4f36-aa94-1df2b74f859b · outbound

This paper cites Optimality and stability in federated learning: A game-theoretic approach.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Optimality and stability in federated learning: A game-theoretic approach

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.259036Z

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 c588d257-8834-4f4e-b754-eac3fd272e30 · outbound

This paper cites Incentivizing honesty among competitors in collaborative learning and optimization.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Incentivizing honesty among competitors in collaborative learning and optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.242358Z

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 6e82a90a-2793-4eac-8e83-69aeb7b03f57 · outbound

This paper cites The role of cross-silo federated learning in facilitating data sharing in the agri-food sector.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning The role of cross-silo federated learning in facilitating data sharing in the agri-food sector

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.225389Z

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 85b7bc59-771f-4489-8026-11be343b881f · outbound

This paper cites Robust federated learning with noisy and heterogeneous clients.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Robust federated learning with noisy and heterogeneous clients

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.209036Z

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 d3ea6567-5a43-4d1d-92f8-b2f00ea5d4e0 · outbound

This paper cites Application of logistic function for analysis of marginal value diminishing laws.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Application of logistic function for analysis of marginal value diminishing laws

Reference 14

Resolution: verified exact; raw_fallback, observed 2026-08-12T04:57:46.838353Z

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 26b8a64c-ca25-40e6-99db-b26a85ae6d0a · outbound

This paper cites Sharp bounds for federated averaging (local sgd) and continuous perspective.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Sharp bounds for federated averaging (local sgd) and continuous perspective

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.192788Z

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 2e51a132-04e5-4713-a87f-28b7326a18b5 · outbound

This paper cites On the Effect of Defections in Federated Learning and How to Prevent Them.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning On the Effect of Defections in Federated Learning and How to Prevent Them

Reference 16

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Source-reported events for the cited work

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Observation 8ef37497-280d-433e-8228-d27dc06d58bc · outbound

This paper cites Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T04:57:46.488213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 217b5c4e-f88c-4004-942f-4c7d7aefee00 · outbound

This paper cites Advances and open problems in federated learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Advances and open problems in federated learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.175297Z

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 4ff3946f-a034-48e6-ab17-e10c8b198668 · outbound

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

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Scaffold: Stochastic controlled averaging for federated learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T04:57:46.497959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f1c6b20-7455-4206-bd78-7c64a17a0b45 · outbound

This paper cites Mechanisms that incentivize data sharing in federated learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Mechanisms that incentivize data sharing in federated learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.150364Z

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 c4607a44-41e4-45fd-89f6-27c08cb65fb4 · outbound

This paper cites Tighter theory for local sgd on identical and heterogeneous data.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Tighter theory for local sgd on identical and heterogeneous data

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.134569Z

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 75d1f00a-68b1-4274-a682-dce6aa71e70c · outbound

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

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning A unified theory of decentralized sgd with changing topology and local updates

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T04:57:46.512323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10a8c7d3-92e2-4b29-9aea-b6713d1c60da · outbound

This paper cites Federated learning for open banking.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Federated learning for open banking

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T04:57:46.516944Z

Source-reported events for the cited work

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Observation 0d199908-4822-4ffe-aaaa-a17b70291850 · outbound

This paper cites Three Approaches for Personalization with Applications to Federated Learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Three Approaches for Personalization with Applications to Federated Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T04:57:46.521650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07cb9218-d797-4733-8f4e-92dd3ed996de · outbound

This paper cites Personalized federated learning through local memorization.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Personalized federated learning through local memorization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.096660Z

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 1cafef1a-c2d4-4d9a-9aba-13abf3f37f28 · outbound

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

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Communication-efficient learning of deep networks from decentralized data

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.079212Z

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 03127110-dc9a-463f-bd45-57e11dc52ed2 · outbound

This paper cites Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-12T04:57:46.654414Z

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 a38a81ff-34a6-4bc6-8288-83d91d1207a1 · outbound

This paper cites Algorithmic Game Theory.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Algorithmic Game Theory

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.062539Z

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 c08ce2af-caed-4fff-8dc4-978e6f053d04 · outbound

This paper cites Federated learning techniques applied to credit risk management: A systematic literature review.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Federated learning techniques applied to credit risk management: A systematic literature review

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.047129Z

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 de49d877-c5b4-4d7a-bf75-48c174bd4eeb · outbound

This paper cites The limits and potentials of local sgd for distributed heterogeneous learning with intermittent communication.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning The limits and potentials of local sgd for distributed heterogeneous learning with intermittent communication

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.030182Z

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 b0a73d34-7c4b-4143-8ff1-db4a6ce87fc2 · outbound

This paper cites Robust aggregation for federated learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Robust aggregation for federated learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:47.013937Z

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 c87a47e5-474f-4936-956e-c67bb3561e42 · outbound

This paper cites The future of digital health with federated learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning The future of digital health with federated learning

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3836ad02-c5b2-47ae-ac58-87d8f6b21cd2 · outbound

This paper cites Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:46.985921Z

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 7fd2bdd1-9f3e-4fd4-aaaf-e73be26a4095 · outbound

This paper cites Federated machine learning in vehicular networks: A summary of recent applications.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Federated machine learning in vehicular networks: A summary of recent applications

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:46.968196Z

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 0278c004-f10e-4e45-b0b8-bb4d67f0852b · outbound

This paper cites Provable mutual benefits from federated learning in privacy-sensitive domains.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Provable mutual benefits from federated learning in privacy-sensitive domains

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:46.953039Z

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 d70a63d7-1d2c-4a1a-a1ee-056e5a9ea6c2 · outbound

This paper cites Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T04:57:46.578291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02244e62-788b-47f3-9436-6fa48ec98edd · outbound

This paper cites Minibatch vs local sgd for heterogeneous distributed learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Minibatch vs local sgd for heterogeneous distributed learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:46.937012Z

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 d0650088-891d-4057-ac8f-6ce0ec3791fb · outbound

This paper cites Byzantine-robust distributed learning: Towards optimal statistical rates.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Byzantine-robust distributed learning: Towards optimal statistical rates

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:46.920577Z

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 1351d5db-6f47-495c-9a7f-c22e74571fe3 · outbound

This paper cites A survey of incentive mechanism design for federated learning.

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning A survey of incentive mechanism design for federated learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:57:46.903728Z

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.