Paper Citation Record · LEDGER

How to Evaluate Participant Contributions in Decentralized Federated Learning

As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.23246.

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

pith.paper-citation-record.v1
2505.23246 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:54:26.666426Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

40 of 40 outbound references displayed

verified exact1
verified fuzzy32
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1e5a03a4-cfe4-4324-b83c-c67ea0d7251a · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data.

How to Evaluate Participant Contributions in Decentralized Federated Learning Communication-Efficient Learning of Deep Networks from Decentralized Data

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:33.904551Z

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Observation 8c25be29-1762-4dd3-a668-2d752c427d84 · outbound

This paper cites Fully decentralized federated learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Fully decentralized federated learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:33.706263Z

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Observation 24976de4-9354-4f23-8aae-19f2d71577d5 · outbound

This paper cites BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning

Reference 3

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Observation a02ce317-8758-41f9-8705-d1efcef60b11 · outbound

This paper cites GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adap- tive Communication.

How to Evaluate Participant Contributions in Decentralized Federated Learning GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adap- tive Communication

Reference 4

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Observation f0e9b4a4-c242-42c3-b22b-5fdb49cbd19f · outbound

This paper cites Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:33.220385Z

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Observation c852a7f7-778c-44d1-92d5-ce60c6c9fd48 · outbound

This paper cites Incentive Mechanisms in Federated Learning and A Game-Theoretical Approach.

How to Evaluate Participant Contributions in Decentralized Federated Learning Incentive Mechanisms in Federated Learning and A Game-Theoretical Approach

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:32.895665Z

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Observation 66ffe706-f3cb-43e2-ab5f-5c5eb2392364 · outbound

This paper cites Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction.

How to Evaluate Participant Contributions in Decentralized Federated Learning Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction

Reference 7

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Source-reported events for the cited work

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Observation dc951c13-0d35-4faa-951c-2c8567a57696 · outbound

This paper cites Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:32.340729Z

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Observation ddc5cb16-28a2-48d9-9013-f8fb85fd7391 · outbound

This paper cites Optimizing Federated Learning on Non-IID Data Using Local Shapley Value.

How to Evaluate Participant Contributions in Decentralized Federated Learning Optimizing Federated Learning on Non-IID Data Using Local Shapley Value

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:32.029041Z

Source-reported events for the cited work

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Observation c81b8322-fe2f-4a14-9c03-7d1320447d82 · outbound

This paper cites Profit Allocation for Federated Learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Profit Allocation for Federated Learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:31.735917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8bebb5ed-3b1b-49df-9760-e31a4d2d739c · outbound

This paper cites GTG- Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning GTG- Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:31.478702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 446fb9ef-1c1d-4091-9ca0-1fa6f7c40be1 · outbound

This paper cites Efficient and Fair Data Valuation for Horizontal Federated Learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Efficient and Fair Data Valuation for Horizontal Federated Learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:31.228452Z

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Observation 19542684-63da-428d-97ad-4a7a48daf7b8 · outbound

This paper cites Toward understanding the influence of individual clients in federated learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Toward understanding the influence of individual clients in federated learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:31.011762Z

Source-reported events for the cited work

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Observation 18cf976d-2959-4f41-858f-637d77d4a8a5 · outbound

This paper cites Measure Contribu- tion of Participants in Federated Learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Measure Contribu- tion of Participants in Federated Learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:30.808961Z

Source-reported events for the cited work

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Observation 250395eb-d483-482f-bdc7-cb37b5da4ee3 · outbound

This paper cites Procaccia.

How to Evaluate Participant Contributions in Decentralized Federated Learning Procaccia

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:30.649168Z

Source-reported events for the cited work

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Observation 22c3a925-c08b-474b-8e05-5ea8ae1aab23 · outbound

This paper cites Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations.

How to Evaluate Participant Contributions in Decentralized Federated Learning Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations

Reference 16

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Observation 5fab3cde-9147-42b6-af49-a4f068be5d67 · outbound

This paper cites SPACE: Single-round Participant Amalgamation for Contribution Evalu- ation in Federated Learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning SPACE: Single-round Participant Amalgamation for Contribution Evalu- ation in Federated Learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:30.446767Z

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Observation 0a0d5966-0260-455f-b37d-698f1bc61995 · outbound

This paper cites Fast, Robust and Interpretable Participant Contribution Estimation for Federated Learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Fast, Robust and Interpretable Participant Contribution Estimation for Federated Learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:30.304745Z

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This paper cites Roth, Wenqi Li, Dong Yang, Can Zhao, Vishwesh Nath, Daguang Xu, Qi Dou, and Ziyue Xu.

How to Evaluate Participant Contributions in Decentralized Federated Learning Roth, Wenqi Li, Dong Yang, Can Zhao, Vishwesh Nath, Daguang Xu, Qi Dou, and Ziyue Xu

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:30.180470Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e0dce4ca-8b6e-40cf-a46b-89bf2bf76807 · outbound

This paper cites Data-free evaluation of user contri- butions in federated learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Data-free evaluation of user contri- butions in federated learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:30.032195Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6e5d2fcd-65e4-4ef6-bce0-ed13952cab24 · outbound

This paper cites A value for n-person games.

How to Evaluate Participant Contributions in Decentralized Federated Learning A value for n-person games

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:29.909088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc1b75c8-9d7a-4d10-a568-d68e8840a758 · outbound

This paper cites Algorithmic stability and sanity-check bounds for leave-one-out cross-validation.

How to Evaluate Participant Contributions in Decentralized Federated Learning Algorithmic stability and sanity-check bounds for leave-one-out cross-validation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:29.745094Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 256a919c-37eb-4bbf-a8c8-9be7f870cae4 · outbound

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How to Evaluate Participant Contributions in Decentralized Federated Learning Unresolved cited work

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation efdd6294-4e8f-4c27-b2a3-b74be8cce080 · outbound

This paper cites TravellingFL: Communication Efficient Peer-to-Peer Federated Learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning TravellingFL: Communication Efficient Peer-to-Peer Federated Learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:29.494975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 26c6faa1-594b-494e-a72e-3726cc6fe252 · outbound

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How to Evaluate Participant Contributions in Decentralized Federated Learning Unresolved cited work

Reference 25

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Observation 76cfd16d-b6f5-4484-86db-025ae3a84a75 · outbound

This paper cites Personalized and private peer-to-peer machine learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Personalized and private peer-to-peer machine learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:29.179278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 23633cd4-9011-473e-a6a8-ae18f3f15412 · outbound

This paper cites Robust estimation and wavelet thresholding in partially linear models.

How to Evaluate Participant Contributions in Decentralized Federated Learning Robust estimation and wavelet thresholding in partially linear models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:28.985815Z

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Observation d3f7ecef-33fc-49c3-8028-b3aa781b536b · outbound

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How to Evaluate Participant Contributions in Decentralized Federated Learning Unresolved cited work

Reference 28

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Source-reported events for the cited work

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Observation a6d554bf-364d-4762-b1d2-bf7024963728 · outbound

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How to Evaluate Participant Contributions in Decentralized Federated Learning MacEachern, and Yoonsuh Jung

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:28.614830Z

Source-reported events for the cited work

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Observation 15a7d259-6237-4f60-8e12-5395378a28c2 · outbound

This paper cites Learning multiple layers of features from tiny images.

How to Evaluate Participant Contributions in Decentralized Federated Learning Learning multiple layers of features from tiny images

Reference 30

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How to Evaluate Participant Contributions in Decentralized Federated Learning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 31

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How to Evaluate Participant Contributions in Decentralized Federated Learning Watts and Steven H

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:28.373217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e272c58a-e4d2-44e7-84c7-4165e23d267b · outbound

This paper cites Impact of Network Topol- ogy on the Convergence of Decentralized Federated Learning Systems.

How to Evaluate Participant Contributions in Decentralized Federated Learning Impact of Network Topol- ogy on the Convergence of Decentralized Federated Learning Systems

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:28.160448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 02719d5e-290d-4e8f-8f98-82f5b2d4aa6c · outbound

This paper cites Data shapley: Equitable valuation of data for machine learning.

How to Evaluate Participant Contributions in Decentralized Federated Learning Data shapley: Equitable valuation of data for machine learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:28.013811Z

Source-reported events for the cited work

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Observation f7318d7f-a3a2-4308-9e33-1fa3acbfcd25 · outbound

This paper cites A new approximation method for the Shapley value applied to the WTC 9/11 terrorist attack.

How to Evaluate Participant Contributions in Decentralized Federated Learning A new approximation method for the Shapley value applied to the WTC 9/11 terrorist attack

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:27.828642Z

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Observation a9bcb5e8-b5c9-4f03-9a33-d5d14b23f8e4 · outbound

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How to Evaluate Participant Contributions in Decentralized Federated Learning Robust and Communication-Efficient Federated Learning From Non-i.i.d

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:27.675517Z

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Observation 5426c82d-82a4-4bef-9a28-9984b10d8ea6 · outbound

This paper cites Eldar, H.

How to Evaluate Participant Contributions in Decentralized Federated Learning Eldar, H

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:27.544910Z

Source-reported events for the cited work

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Observation 6f578a84-8cba-45dc-a249-c69f80a7199f · outbound

This paper cites The Byzantine generals strike again.

How to Evaluate Participant Contributions in Decentralized Federated Learning The Byzantine generals strike again

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:27.303780Z

Source-reported events for the cited work

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Observation 5a24f40f-efa0-4546-a4c8-1ce7088cb57d · outbound

This paper cites On Byzantine Broadcast in Loosely Connected Networks.

How to Evaluate Participant Contributions in Decentralized Federated Learning On Byzantine Broadcast in Loosely Connected Networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:54:27.132427Z

Source-reported events for the cited work

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Observation ea66add3-f8eb-447d-8536-eae9839fb979 · outbound

This paper cites Byzantine Reliable Broadcast with Low Communication and Time Complexity.

How to Evaluate Participant Contributions in Decentralized Federated Learning Byzantine Reliable Broadcast with Low Communication and Time Complexity

Reference 40

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.